FINAL DRAFT Chapter 2 IPCC WGII Sixth Assessment Report
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2 Chapter 2: Terrestrial and Freshwater Ecosystems and their Services
3
4 Coordinating Lead Authors: Camille Parmesan (France/USA/United Kingdom), Mike D. Morecroft
5 (United Kingdom), Yongyut Trisurat (Thailand)
6
7 Lead Authors: Rita Adrian (Germany), Gusti Zakaria Anshari (Indonesia), Almut Arneth (Germany),
8 Qingzhu Gao (China), Patrick Gonzalez (USA), Rebecca Harris (Australia), Jeff Price (United Kingdom),
9 Nicola Stevens (South Africa), Gautam Hirak Talukdar (India)
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11 Contributing Authors: David D Ackerly (USA), Elizabeth Anderson (USA), Vanessa Bremerich
12 (Germany), Lluís Brotons (Spain), Yu-Yun Chen (Taiwan, Province of China), Meghnath Dhimal (Nepal),
13 Sami Domisch (Germany), Errol Douwes (South Africa), Alexander Flecker (USA), Wendy Foden (South
14 Africa), Rachael V. Gallagher (Australia), Michael Goulding (USA), Aurora Gaxiola (Chile), Kerry-Anne
15 Grey (South Africa), Susan Harrison (USA), David A. Keith (Australia), Benjamin M. Kraemer
16 (USA/Germany), Simone Langhans (Switzerland), Andrew Latimer (USA), Julie Loisel (Canada, USA),
17 James Pearce-Higgins (United Kingdom), Guy Midgley (South Africa), Erin Mordecai (USA), Francisco
18 Moreira (Portugal), Isla Myers-Smith (United Kingdom/USA), A. Townsend Peterson (USA), Julio C.
19 Postigo (Peru/USA), Joacim Rocklöv (Sweden), Angela Gallego-Sala (Spain/United Kingdom), Nathalie
20 Seddon (United Kingdom), Michael C. Singer (France/United Kingdom), Jasper Slingsby (South Africa),
21 Stavana E. Strutz (USA), Merritt Turetsky (USA), Beth Turner (Canada), Kenneth Young (USA).
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23 Review Editors: Carol Franco Billini (Dominican Republic/USA), Yakiv Didukh (Ukraine), Andreas
24 Fischlin (Switzerland)
25
26 Chapter Scientists: Stavana E. Strutz (USA), Dalila Mezzi-Booth (France)
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28 Date of Draft: 1 October 2021
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30 Notes: TSU Compiled Version
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32
33 Table of Contents
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35 Executive Summary.......................................................................................................................................... 3
36 2.1 Introduction .............................................................................................................................................. 9
37 2.1.1 Overview ......................................................................................................................................... 9
38 2.1.2 Points of Departure......................................................................................................................... 9
39 2.1.3 Guide to Attribution and Traceability of Uncertainty Assessments.............................................. 10
40 2.2 Connections of Ecosystem Services to Climate Change...................................................................... 11
41 2.3 Hazards and Exposure ........................................................................................................................... 13
42 2.3.1 Observed Changes to Hazards and Extreme Events..................................................................... 13
43 2.3.2 Projected Impacts of Extreme Events ........................................................................................... 15
44 2.3.3 Biologically Important Physical Changes in Freshwater Systems ............................................... 16
45 Cross-Chapter Box EXTREMES: Ramifications of Climatic Extremes for Marine, Terrestrial,
46 Freshwater and Polar Natural Systems................................................................................................ 22
47 2.4 Observed Impacts of Climate Change on Species, Communities, Biomes, Key Ecosystems and
48 their Services ........................................................................................................................................... 26
49 2.4.1 Overview ....................................................................................................................................... 26
50 2.4.2 Observed Responses to Climate Change by Species and Communities (Freshwater and
51 Terrestrial) ............................................................................................................................ 27
52 FAQ2.1: Will species go extinct with climate change and is there anything we can do to prevent it? ... 29
53 FAQ2.2: How does climate change increase the risk of diseases?.............................................................. 40
54 2.4.3 Observed Changes in Key Biomes, Ecosystems and their Services .............................................. 44
55 2.4.4 Observed Changes in Ecosystem Processes and Services ............................................................ 52
56 FAQ2.3: Is climate change increasing wildfire? .......................................................................................... 57
57 2.4.5 Conclusions on Observed Impacts ................................................................................................ 64
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1 2.5 Projected Impacts and Risk for Species, Communities, Biomes, Key Ecosystems and their
2 Services .................................................................................................................................................... 68
3 2.5.1 Projected Changes at Species and Community Levels ................................................................. 68
4 2.5.2 Projected Changes at Level of Biomes and Whole Ecosystems .................................................... 75
5 Box 2.1: Assessing Past Projections of Ecosystem Change Against Observations ................................... 84
6 2.5.3 Risk Assessment of Ecosystems and Related Services .................................................................. 86
7 2.5.4 Key Risks to Terrestrial and Freshwater Ecosystems from Climate Change .............................. 98
8 FAQ2.4: How does nature benefit human health and well-being and how does climate change affect
9 this? ........................................................................................................................................................ 103
10 2.6 Climate Change Adaptation for Terrestrial and Freshwater Ecosystems ..................................... 105
11 2.6.1 Limits to Autonomous (Natural) Adaptation............................................................................... 106
12 2.6.2 Adaptation for Biodiversity Conservation .................................................................................. 107
13 2.6.3 Nature-based Solutions: Ecosystem-based Adaptation .............................................................. 111
14 2.6.4 Adaptation for Increased Risk of Disease ................................................................................... 115
15 Cross-Chapter Box ILLNESS: Infectious Diseases, Biodiversity and Climate: Serious Risks Posed by
16 Vector- and Water-borne Diseases ..................................................................................................... 116
17 2.6.5 Adaptation in Practice: Case Studies and Lessons Learned ...................................................... 121
18 2.6.6 Limits to Adaptation Actions by People ...................................................................................... 129
19 2.6.7 Climate Resilient Development ................................................................................................... 130
20 Cross-Chapter Box NATURAL: Nature-Based Solutions for Climate Change Mitigation and
21 Adaptation ............................................................................................................................................. 132
22 FAQ2.5: How can we reduce the risk of climate change to people by protecting and managing nature
23 better? .................................................................................................................................................... 140
24 2.6.8 Feasibility of Adaptation Options ............................................................................................... 142
25 Box 2.2: Risks of Maladaptive Mitigation .................................................................................................. 145
26 FAQ2.6: Can tree planting tackle climate change? ................................................................................... 146
27 2.7 Reducing Scientific Uncertainties to Inform Policy and Management Decisions .......................... 148
28 2.7.1 Observed impacts ........................................................................................................................ 148
29 2.7.2 Projected risks ............................................................................................................................ 149
30 2.7.3 Adaptation and Climate Resilient Development ......................................................................... 149
31 References...................................................................................................................................................... 150
32 Large Tables.................................................................................................................................................. 232
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FINAL DRAFT Chapter 2 IPCC WGII Sixth Assessment Report
1 Executive Summary
2
3 Chapter 2, building upon prior assessments1 provides a global assessment of observed impacts and projected
4 risks of climate change to terrestrial and freshwater ecosystems, including their component species and the
5 services they provide to people. Where possible, differences among regions, taxonomic groups and
6 ecosystems types are presented. Adaptation options to reduce risks to ecosystems and people are assessed.
7
8 Observed Impacts
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10 Multiple lines of evidence, combined with strong and consistent trends observed on every continent,
11 make it very likely2 that many observed changes in ranges, phenology, physiology and morphology of
12 terrestrial and freshwater species can be attributed to regional and global climate changes,
13 particularly increases in frequency and severity of extreme events (very high confidence3) {2.3.1;
14 2.3.3.5; 2.4.2; 2.4.5; Table 2.2; Table 2.3; Table 2.S.1; Cross-Chapter Box EXTREMES this Chapter}. The
15 most severe impacts are occurring in the most vulnerable species and ecosystems, characterized by inherent
16 physiological, ecological or behavioural traits that limit their abilities to adapt and those most exposed to
17 climatic hazards (high confidence) {2.4.2.2; 2.4.2.6; 2.4.2.8; 2.4.5; 2.6.1; Cross-Chapter Box EXTREMES
18 this Chapter}.
19
20 New studies since AR5 and SR1.5 (now >12,000 species globally) show changes consistent with climate-
21 change. Where attribution was assessed (>4,000 species globally) approximately half of species had
22 shifted their ranges to higher latitudes or elevations and two-thirds of spring phenology had advanced,
23 driven by regional climate changes (very high confidence). Shifts in species ranges are altering
24 community make-up, with exotic species exhibiting greater ability to adapt to climate change than natives,
25 especially in more northern latitudes, potentially leading to newly invasive species {2.4.2.3.3}. New
26 analyses demonstrate that prior reports underestimated impacts due to complex biological responses to
27 climate change (high confidence). {2.4.2.1; 2.4.2.3; 2.4.2.4; 2.4.2.5; 2.4.5; Table 2.2; Table SM2.1; Table
28 2.3}
29
30 Responses in freshwater species are strongly related to changes in the physical environment (high
31 confidence){2.3.3; 2.4.2.3.2}. Global coverage of quantitative observations in freshwater ecosystems has
32 increased since AR5. Water temperature has increased in rivers (up to 1°C decade-1) and lakes (up to 0.45°C
33 decade-1) {2.3.3.1; Figure 2.2}. Extent of ice cover has declined by 25% and duration by >2 weeks {2.3.3.4;
34 Figure 2.4}. Changes in flow have led to reduced connectivity in rivers (high confidence) {2.3.3.2; Figure
35 2.3}. Indirect changes include alterations in river morphology, substrate composition, oxygen concentrations
36 and thermal regime in lakes (very high confidence) {2.3.3.2; 2.3.3.3}. Dissolved oxygen concentrations have
37 typically declined and primary productivity increased with warming. Warming and browning (increase in
38 organic matter) have occurred in boreal freshwaters with both positive and negative repercussions on water
39 temperature profiles (lower vs. upper water)(high confidence) and primary productivity (medium confidence)
40 and reduced water quality (high confidence) {2.4.4.1; Figure 2.5}.
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1
Previous IPCC assessments include the IPCC Fifth Assessment Report (AR5) (IPCC, 2013; IPCC, 2014c; IPCC,
2014d; IPCC, 2014a), the Special Report on Global Warming of 1.5°C (SR1.5) (IPCC, 2014b), the Special Report on
Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC, 2019b) and the IPCC Sixth Assessment Report
Working Group I (AR6 WGI).
2
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result:
Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as likely as not 33–66%,
Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–
100%, More likely than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed
likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the
assessed likelihood of an outcome lies within the 17-83% probability range.
3
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust;
and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very
low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of
agreement are correlated with increasing confidence.
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1 Climate change has increased wildlife diseases (high confidence). Experimental studies provide high
2 confidence in attribution of observed increased disease severity, outbreak frequency and emergence of novel
3 vectors and their diseases into new areas to recent trends in climate and extreme events. Many vector-borne
4 diseases, and those caused by ticks, helminth worms and Bd (chytrid) fungus, have shifted poleward,
5 upward, and are emerging in new regions (high confidence).In the high Arctic and high elevations of Nepal,
6 there is high confidence that climate change has driven expansion of vector-borne diseases that infect
7 humans. {2.4.2.7, 7.2.2.1, 9.8.2.4, 10.4.7.1, 12.3.1.4, 13.7.1.2, 14.4.6.4, Cross-Chapter Box ILLNESS this
8 Chapter}
9
10 Forest insect pests have expanded northward and severity and outbreak extent has increased in
11 northern North America, northern Eurasia, due to warmer winters reducing mortality and longer
12 growing seasons favouring more generations per year (high confidence) {2.4.2.1}
13
14 Climate-caused local population extinctions have been widespread among plants and animals, detected
15 in 47% of 976 species examined and associated with increases in hottest yearly temperatures (very high
16 confidence) {2.4.2.2}. Climate-driven population extinctions have been higher in tropical (55%), than
17 temperate habitats (39%), higher in freshwater (74%), than in marine (51%) or terrestrial (46%) habitats and
18 higher in animals (50%) than in plants (39%). Extreme heat waves has led to local fish kills in lakes
19 {2.3.3.5}. Intensification of droughts contributes to disappearance of small or ephemeral ponds, which often
20 hold rare and endemic species. {2.4.2.2; Cross-Chapter Box EXTREMES this Chapter}
21
22 Global extinctions or near-extinctions have been linked to regional climate change in three
23 documented cases {2.4.2.2}. The cloud-forest-restricted Golden toad (Incilius periglenes) was extinct by
24 1990 in a nature preserve in Costa Rica following successive extreme droughts (medium confidence). The
25 white sub-species of the lemuroid ringtail possum (Hemibelideus lemuroides) in Queensland, Australia,
26 disappeared after heatwaves in 2005 (high confidence): intensive censuses found only 2 individuals in 2009.
27 The Bramble Cays Melomys (Melomys rubicola), was not seen after 2009 and declared extinct in 2016, with
28 SLR and increased storm surge, associated with climate change, the most probable drivers (high confidence).
29 The interaction of climate change and chytrid fungus (Bd) has driven many of the observed global
30 amphibian declines and species' extinctions (robust evidence, high agreement) {2.4.2.7.1}.
31
32 A growing number of studies document genetic evolution within populations in response to recent climate
33 change (very high confidence). To date, genetic changes remain within the limits of known variation for
34 species (high confidence). Controlled selection experiments and field observations indicate that
35 evolution would not prevent a species becoming extinct if its climate space disappears globally (high
36 confidence). Climate hazards outside those to which species are adapted are occurring on all continents
37 (high confidence). More frequent and intense extreme events, superimposed on longer-term climate trends,
38 have pushed sensitive species and ecosystems towards tipping points, beyond ecological and evolutionary
39 capacity to adapt, causing abrupt and possibly irreversible changes (medium confidence). {2.3.1; 2.3.3;
40 2.4.2.6; 2.4.2.8; 2.6.1; Cross-Chapter Box ILNESS this Chapter, Cross-Chapter Box EXTREMES this
41 Chapter}
42
43 Since AR5, biome shifts and structural changes within ecosystems have been detected at an increasing
44 number of locations, consistent with climate change and increasing atmospheric CO2 (high
45 confidence). New studies document changes that were projected in prior reports, including upward shifts in
46 the forest/alpine tundra ecotone, northward shifts in the deciduous/boreal forest ecotones, increased woody
47 vegetation in sub-Arctic tundra, and shifts in thermal habitat in lakes. A combination of changes in grazing,
48 browsing, fire, climate, and atmospheric CO2 are leading to observed woody encroachment into grasslands
49 and savannas, consistent with projections from process-based models driven by precipitation, atmospheric
50 CO2 and wildfire (high confidence). {2.4.3; Table 2.3; Table 2.S.1; Box 2.1; Figure Box 2.1.1; Table Box
51 2.1.1} There is high agreement between projected changes in earlier reports and recent observed trends for
52 areas of increased tree death in temperate and boreal forests and of woody encroachment in savannas,
53 grasslands and tundra {2.5.4; Box 2.1; Figure Box 2.1.1; Table Box 2.1.1}. Observed changes impact
54 structure, functioning and resilience of ecosystems, and ecosystem services such as climate regulation (high
55 confidence). {2.3; 2.4.2; 2.4.3; 2.4.4, 2.5.4, Figure 2.11, Table 2.5, Box 2.1; Figure Box 2.1.1; Table Box
56 2.1.1}
57
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1 Regional increases in area burned by wildfires (up to double natural levels), tree mortality up to 20%,
2 and biome shifts up to 20 km latitudinally and 300 m upslope, have been attributed to anthropogenic
3 climate change in tropical, temperate and boreal ecosystems around the world (high confidence),
4 damaging key aspects of ecological integrity. This degrades vegetation survival, habitat for biodiversity,
5 water supplies, carbon sequestration, and other key aspects of the integrity of ecosystems and their ability to
6 provide services for people (high confidence). {2.4.3.1, 2.4.4.2, 2.4.4.3, 2.4.4.4; Table 2.3; Table 2.S.1}
7
8 B.11 Fire seasons have lengthened on one-quarter of vegetated area since 1979 as a result of increasing
9 temperature, aridity, and drought (medium confidence). Field evidence shows that anthropogenic climate
10 change has increased the area burned by wildfire above natural levels in western North America from
11 1984-2017 by double for the Western USA and 11 time higher than natural in one extreme year in
12 British Columbia (high confidence). Burned area has increased in the Amazon, the Arctic, Australia, and
13 parts of Africa and Asia, consistent with, but not formally attributed to anthropogenic climate change.
14 Wildfires generate up to one-third of global ecosystem carbon emissions, a feedback that exacerbates climate
15 change (high confidence). Deforestation, peat draining, agricultural expansion or abandonment, fire
16 suppression, and inter-decadal cycles such as the El Niño-Southern Oscillation, can exert a stronger
17 influence than climate change on increasing or decreasing wildfire. {2.4.4.2; Table 2.3; Table 2.S.1;
18 FAQ2.1}. Increases in wildfire from levels to which ecosystems are adapted degrades vegetation, habitat for
19 biodiversity, water supplies, and other key aspects of the integrity of ecosystems and their ability to provide
20 services for people (high confidence). {2.4.3.1, 2.4.4.2, 2.4.4.3, 2.4.4.4; Table 2.3; Table 2.S.1}
21
22 Anthropogenic climate change has caused drought-induced tree mortality of up to 20% in the period
23 1945-2007 in three regions in Africa and North America. It has also potentially contributed to over 100
24 other cases of drought-induced tree mortality across Africa, Asia, Australia, Europe, and North and South
25 America (high confidence). Field observations document post-mortality vegetation shifts (high confidence).
26 Timber cutting, agricultural expansion, air pollution, and other non-climate factors also contribute to tree
27 death. Climate-change driven increases in forest insect pests have contributed to mortality and changes in
28 carbon dynamics in many temperate and boreal forest areas (very high confidence). The direction of changes
29 in carbon balance and wildfires following insect outbreaks depends on the local forest-insect communities
30 (medium confidence). {2.4.4.3; Table 2.3; Table 2.S.1}.
31
32 Terrestrial ecosystems currently remove more carbon from the atmosphere, 2.5-4.3 Gt y-1, than they
33 emit. Intact tropical rainforests, Arctic permafrost, and other healthy high carbon ecosystems provide
34 a vital global ecosystem service of preventing release of stored carbon (high confidence). Terrestrial
35 ecosystems contain stocks of ~3500 GtC in vegetation, permafrost, and soils, three to five times the amount
36 of carbon in unextracted fossil fuels (high confidence), and >4 times the carbon currently in the atmosphere
37 (high confidence). Tropical forests and Arctic permafrost contain the highest ecosystem carbon, with
38 peatlands following (high confidence). Deforestation, draining and burning of peatlands, and thawing of
39 Arctic permafrost due to climate change shifts these ecosystems from carbon-sinks to carbon-sources (high
40 confidence). {2.4.3.6; 2.4.3.8; 2.4.3.9. 2.4.4.4}
41
42 Evidence indicates that climate change is affecting many species, ecosystems, and ecological processes
43 that provide ecosystem services connected to human health, livelihoods, and well-being (medium
44 confidence). These services include climate regulation, water and food provisioning, pollination of crops,
45 tourism and recreation. It is difficult establish end-to-end attribution from climatic changes to changes in a
46 given ecosystem service and to identify the location and timing of impacts. This limits specific adaptation
47 planning, but protection and restoration of ecosystems could build resilience of service provision.{2.2; 2.3;
48 2.4.2.7; 2.4.5; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.7; Cross-Chapter Box NATURAL this Chapter; Cross-Chapter
49 Box ILLNESS this Chapter; Cross-Chapter Box EXTREMES this Chapter; Cross-Chapter Box COVID in
50 Chapter 7; Cross-Chapter Box MOVING PLATE in Chapter 5}
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52 Projected Risks
53
54 Climate change increases risks to fundamental aspects of terrestrial and freshwater ecosystems, with
55 the potential for species' extinctions to reach 60% at 5°C GSAT warming (high confidence), biome
56 shifts (changes in the major vegetation form of an ecosystem) on 15% (at 2ºC warming) to 35% (at
57 4ºC warming) of global land (medium confidence), and increases in the area burned by wildfire of 35%
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1 (at 2ºC warming) to 40% (at 4ºC warming) of global land (medium confidence). {2.5.1; 2.5.2; 2.5.3;
2 2.5.4; Figure 2.6; Figure 2.7; Figure 2.8; Figure 2.9; Figure 2.11; Table 2.5; Table 2.S.2; Table 2.S.4; Cross-
3 Chapter Box DEEP in Chapter 1; Cross-Chapter Paper 1}
4
5 Extinction of species is an irreversible impact of climate change, the risk of which increases steeply
6 with rises in global temperature. It is likely that the percentage of species at high risk of extinction (median
7 and maximum estimates) will be 9% (max 14%) at 1.5°C, 10% (max 18%) at 2°C, 12% (max 29%) at 3.0°C,
8 13% (max 39%) at 4°C and 15% (max 48%) at 5°C (Figure 2.7). Among groups containing largest numbers
9 of species at high risk of extinctions for mid-levels of warming (3.2°C) are: invertebrates (15%), specifically
10 pollinators (12%), amphibians (11%, but salamanders are at 24%) and flowering plants (10%). All groups
11 fare substantially better at 2°C, with extinction projections reducing to <3% for all groups, except
12 salamanders at 7% (medium confidence) (Figure 2.8a). Even the lowest estimates of species' extinctions
13 (9%) are 1000x natural background rates. Projected species' extinctions at future global warming levels are
14 consistent with projections from AR4, but assessed on many more species with much greater geographic
15 coverage and a broader range of climate models. {2.5.1.3; Figure 2.6; Figure 2.7; Figure 2.8; Cross-Chapter
16 Box DEEP in Chapter1; Cross-Chapter Paper 1}
17
18 Species are the fundamental unit of ecosystems, and increasing risk to species increases risk to
19 ecosystem integrity, functioning and resilience with increasing warming (high confidence). As species
20 become rare, their roles in the functioning of the ecosystem diminishes (high confidence). Loss of species
21 reduces the ability of an ecosystem to provide services and lowers its resilience to climate change (high
22 confidence). At 1.58°C (median estimate), >10% of species are projected to become endangered (sensu
23 IUCN); at 2.07°C (median) >20% of species are projected to become endangered, representing high and very
24 high biodiversity risk, respectively (medium confidence){2.5.4; Figure 2.8b, Figure 2.11; Table 2.5, Table
25 2.S.4}. Biodiversity loss is projected for more regions with increasing warming, and to be worst in northern
26 South America, southern Africa, most of Australia, and northern high latitudes (medium confidence){2.5.1.3;
27 Figure 2.6}.
28
29 Climate change increases risks of biome shifts on up to 35% of global land at ≥4ºC warming, that
30 emissions reductions could limit to <15% for <2ºC warming (medium confidence). Under high
31 warming scenarios, models indicate shifts of extensive parts of the Amazon rainforest to drier and
32 lower-biomass vegetation (medium confidence), poleward shifts of boreal forest into treeless tundra
33 across the Arctic, and upslope shifts of montane forests into alpine grassland (high confidence). Area at
34 high risk of biome shifts from climate change and land use change combined can double or triple compared
35 to climate change alone (medium confidence). Novel ecosystems, with no historical analogue, are expected
36 to become increasingly common in future (medium confidence). {2.3, 2.4.2.3.3, 2.5.2; 2.5.4, Figure 2.11;
37 Table 2.5; Table 2.S.4}
38
39 Risk of wildfires increases with global temperature (high confidence). With 4ºC warming by 2100
40 wildfire frequency is projected to have a net increase of ~30% (medium confidence). Increased wildfire,
41 combined with soil erosion due to deforestation, could degrade water supplies (medium confidence). For
42 ecosystems with historically low fire frequencies, a projected 4ºC global temperature rise increases risks of
43 fire, with potential increases in tree mortality and conversion of extensive parts of Amazon rainforest to drier
44 and lower-biomass vegetation (medium confidence). {2.5.3.2; 2.5.3.3}
45
46 Continued climate change substantially increases risk of carbon stored in the biosphere being released
47 into the atmosphere due to increases in processes such as wildfires, tree mortality, insect pest
48 outbreaks, peatland drying and permafrost thaw (high confidence). These phenomena exacerbate self-
49 reinforcing feedbacks between emissions from high-carbon ecosystems (that currently store ~3030–4090
50 GtC) and increasing global temperatures. Complex interactions of climate change, land use change, carbon
51 dioxide fluxes, and vegetation changes, combined with insect outbreaks and other disturbances, will regulate
52 the future carbon balance of the biosphere, processes incompletely represented in current earth system
53 models. The exact timing and magnitude of climate-biosphere feedbacks and potential tipping points of
54 carbon loss are characterized by large uncertainty, but studies of feedbacks indicate increased ecosystem
55 carbon losses can cause large future temperature increases (medium confidence). {AR6 WGI 5.4, Table 5.4,
56 Figure 5.29; 2.5.2.7; 2.5.2.8; 2.5.2.9; 2.5.3.2; 2.5.3.3; 2.5.3.4; 2.5.3.5; Figure 2.10; Figure 2.11; Table 2.4;
57 Table 2.5; Table 2.S.2; Table 2.S.4}
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1
2 Contributions of Adaptation Measures to Solutions
3
4 The resilience of biodiversity and ecosystem services to climate change can be increased by human
5 adaptation actions including ecosystem protection and restoration (high confidence). Ecological theory
6 and observations show that a wide range of actions can reduce risks to species and ecosystem integrity. This
7 includes minimising additional stresses or disturbances, reducing fragmentation, increasing natural habitat
8 extent, connectivity and heterogeneity, maintaining taxonomic, phylogenetic and functional diversity and
9 redundancy; and protecting small-scale refugia where microclimate conditions can allow species to persist
10 (high confidence). Adaptation also includes actions to aid the recovery of ecosystems following extreme
11 events. Understanding the characteristics of vulnerable species can assist in early warning systems to
12 minimise negative impacts and inform management intervention. {2.3; Figure 2.1; 2.5.3.1, 2.6.2, Table 2.6,
13 2.6.5, 2.6.7, 2.6.8}.
14
15 There is new evidence that species can persist in refugia where conditions are locally cooler, when they
16 are declining elsewhere (high confidence) {2.6.2}. Protecting refugia, for example where soils remain wet
17 during drought or fire risk is reduced, and in some cases creating cooler microclimates, are promising
18 adaptation measures {2.6.3; 2.6.5; CCP1; CCP5.2.1}. There is also new evidence that species can persist
19 locally because of plasticity, including changes in phenology or behavioural changes that move an individual
20 into cooler micro-climates, and genetic adaptation may allow species to persist for longer than might be
21 expected from local climatic changes (high confidence) {2.4.2.6; 2.4.2.8, 2.6.1}. There is no evidence to
22 indicate that these mechanisms will prevent global extinctions of rare, very localised species at their climatic
23 limits or species inhabiting climate/habitat zones that are disappearing (high confidence). {2.4.2.8, 2.5.1,
24 2.5.3.1, 2.5.4, 2.6.1, 2.6.2, 2.6.5}
25
26 Since AR5, many adaptation plans and strategies have been developed to protect ecosystems and
27 biodiversity but there is limited evidence of the extent to which adaptation is taking place and virtually
28 no evaluation of the effectiveness of adaptation measures in the scientific literature (medium
29 confidence). This is an important evidence gap that needs to be addressed to ensure a baseline is available
30 against which to judge effectiveness and develop and refine adaptation in future. Many proposed adaptation
31 measures have not been implemented (low confidence) {2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.8; 2.7}
32
33 Ecosystem restoration and resilience building cannot prevent all impacts of climate change, and
34 adaptation planning needs to manage inevitable changes to species distributions, ecosystem structure
35 and processes (very high confidence). Actions to manage inevitable change include local modification of
36 microclimate or hydrology, adjustment of site management plans and facilitating the dispersal of vulnerable
37 species to new locations, both by increasing habitat connectivity or by active translocations of species.
38 Adaptation can reduce risks but cannot prevent all damaging impacts so is not a substitute for reductions in
39 greenhouse gas emissions (high confidence). {2.2; 2.3; 2.3.1; 2.3.2; 2.4.5; 2.5.1.3; 2.5.1.4; 2.5.2; 2.5.3.1;
40 2.5.3.5; 2.5.4; 2.6.1; 2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.8; Cross-Chapter Box NATURAL this Chapter}.
41
42 Ecosystem-base Adaptation (EbA)can deliver climate change adaptation for people with multiple
43 additional benefits, including for biodiversity (high confidence). An increasing body of evidence
44 demonstrates that climatic risks to people, including from flood, drought, fire and over-heating, can be
45 lowered by a range of Ecosystem-based Adaptation techniques in urban and rural areas (medium confidence).
46 EbA forms part of a wider range Nature-based Solutions (NbS) actions and some have mitigation co-
47 benefits, including the protection and restoration of forests and other high-carbon ecosystems, as well as
48 agroecological farming practices {2.6.3; 2.6.5; Cross-Chapter Box NATURAL this Chapter}. However, EbA
49 and other NbS are still not widely implemented. {2.2; 2.5.3.1; 2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6, 2.6.7; Table
50 2.7; Cross-Chapter Box NATURAL this Chapter; Cross-Chapter Paper 1}.
51
52 To realise potential benefits and avoid harm, it is essential that EbA is deployed in the right places and
53 with the right approaches for that area, with inclusive governance (high confidence). Interdisciplinary
54 scientific information and practical expertise, including local and Indigenous knowledge, are essential to
55 effectiveness (high confidence). There is a large risk of maladaptation where this does not happen (high
56 confidence). {1.4.2; 2.2; 2.6; Table 2.7; Box 2.2; Figure Box 2.2.1; Cross-Chapter Box NATURAL this
57 Chapter; Cross-Chapter Paper1; 5.14.2}.
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1
2 Ecosystem-based Adaptation and other Nature-based Solutions are themselves vulnerable to climate
3 change impacts. They need to take account of climate change adaptation if they are to remain effective and
4 will increasingly be under threat at higher warming levels. Nature-based Solutions cannot be regarded as an
5 alternative to, or a reason to delay, deep cuts in greenhouse gas emissions (high confidence). {2.6.3, 2.6.5;
6 2.6.7; Cross-Chapter Box NATURAL this Chapter}
7
8 Climate Resilient Development
9
10 Protection and restoration of natural and semi-natural ecosystems are key adaptation measures in
11 light of clear evidence that damage and degradation of ecosystems exacerbates the impacts of climate
12 change on biodiversity and people (high confidence). Ecosystem services that are at threat from a
13 combination of climate change and other anthropogenic pressures include climate change mitigation, flood
14 risk management, food provisioning and water supply (high confidence). Adaptation strategies that treat
15 climate, biodiversity and human society as coupled systems will be most effective. {2.3; Figure 2.1; 2.5.4;
16 2.6.2; 2.6.3; 2.6.7; Cross-Chapter Box NATURAL and Cross-Chapter Box ILLNESS this Chapter}
17
18 A range of analyses have concluded that ~30% of Earth's surface needs to be effectively conserved to
19 maintain biodiversity and ecosystem services (medium confidence). Climate change places additional
20 stress on ecosystem integrity and functioning, adding urgency for taking action. Low intensity,
21 sustainable management, including by Indigenous peoples, is an integral part of some protected areas and
22 can support effective adaptation and maintain ecosystem health. Food and fibre production in other areas
23 will need to be efficient, sustainable and adapted to climate change to meet the needs of the human
24 population (high confidence). {Figure 2.1; 2.5.4; 2.6.2; 2.6.3; 2.6.7}
25
26 Natural ecosystems can provide carbon storage and sequestration at the same time as providing
27 multiple other ecosystem services, including EbA (high confidence) but there are risks of
28 maladaptation and environmental damage from some approaches to land-based mitigation (high
29 confidence). Plantation forests in areas which would not naturally support forest, including savannas, natural
30 grasslands and temperate peatlands, or replacing native tropical forests on peat soils, have destroyed local
31 biodiversity and created a range of problems, including for water supply, food supply, fire risk and
32 greenhouse gas emissions. Large scale deployment of bioenergy, including Bioenergy with Carbon Capture
33 and Storage (BECCS) through dedicated herbaceous or woody bioenergy crops and non-native production
34 forests can damage ecosystems directly or through increasing competition for land. {2.6.3, 2.6.5, 2.6.6,
35 2.6.7; Box 2.2; Cross-Chapter Box NATURAL this Chapter; CCP7.3.2; Cross-Working Group Box
36 BIOECONOMY in Chapter 5}.
37
38 Terrestrial and aquatic ecosystems and species are often less degraded in lands managed by
39 Indigenous Peoples and local communities than in other lands (medium confidence). Including
40 indigenous and local institutions is a key element in developing successful adaptation strategies. Indigenous
41 and local knowledge contain a wide variety of resource-use practices and ecosystem stewardship strategies
42 that conserve and enhance both wild and domestic biodiversity. {2.6.5; 2.6.7; Cross-Chapter Box
43 NATURAL this Chapter}
44
45 Extreme events are compressing the timeline available for natural systems to adapt and impeding our
46 ability to identify, develop and implement solutions (medium confidence). There is now an urgent need to
47 build resilience and assist recovery following extreme events. This, combined with long-term changes in
48 baseline conditions means that implementation of adaptation and mitigation measures cannot be delayed if
49 they are to be fully effective. {2.3; Cross-Chapter Box EXTREMES this Chapter}
50
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1 2.1 Introduction
2
3 2.1.1 Overview
4
5 Chapter 2: We provide assessments of observed and projected impacts of climate change across species,
6 biomes (vegetation types), ecosystems and ecosystem services, highlighting processes emerging on a global
7 scale. Where sufficient evidence exists, differences in biological responses among regions, taxonomic groups
8 or types of ecosystems are presented, particularly when such differences provide meaningful insights into
9 current or potential future autonomous or human-mediated adaptations. Human interventions that might
10 build resilience of ecosystems and minimise negative impacts of climate change on biodiversity and
11 ecosystem functioning are assessed. Such interventions include adaptation strategies and programmes to
12 support biodiversity conservation and Ecosystem-based Adaptation (EbA). The assessments were done in the
13 context of the Convention on Biological Diversity (CBD) and Sustainable Development Goals (SDGs),
14 whose contributions to climate-resilient development pathways are assessed. This chapter highlights both
15 successes and failures of adaptation attempts and considers potential synergies and conflicts with land-based
16 climate change mitigation. Knowledge gaps and sources of uncertainty are included to encourage additional
17 research.
18
19 The Working Group II Summary for Policy Makers of the 5th Assessment Report (WGII AR5 SPM) stated
20 that “many terrestrial and freshwater species have shifted their geographic ranges, seasonal activities,
21 migration patterns, abundances, and species interactions in response to ongoing climate change” (IPCC,
22 2014e). Based on long-term observed changes across the regions, it was estimated that approximately 20–
23 30% of plant and animal species are at risk of extinction when global mean temperatures rise 2‒3°C above
24 preindustrial levels (Fischlin et al., 2007). In addition, WGII AR5 (IPCC, 2014f) broadly suggested that
25 autonomous adaptation by ecosystems and wild species might occur, and proposed human-assisted
26 adaptation to minimise negative climate change impacts.
27
28 Risk assessments for species, communities, key ecosystems and their services were based on the Risk
29 Assessment Framework introduced in the IPCC AR5 (IPCC, 2014). Assessments of observed changes in
30 biological systems emphasise detection and attribution of climate change on ecological and evolutionary
31 processes with an emphasis on freshwater ecosystems, and assess ecosystem processes that were lightly
32 assessed in previous reports, such as wildfire. Where appropriate, assessment of interactions between climate
33 change and other human activities is provided.
34
35 Land-use and land cover change (LULCC), and unsustainable exploitation of resources from terrestrial and
36 freshwater systems continue to be a major factor of natural ecosystem and biodiversity loss (high
37 confidence). Fertiliser input, pollution of waterways, dam construction and extraction of freshwater for
38 irrigation put additional pressure on biodiversity and alter ecosystem function (Shin et al., 2019). Likewise,
39 for biodiversity, invasive alien species have been identified as a major threat, especially in freshwater
40 systems, islands and coastal regions (high confidence) (IPBES, 2018b; IPBES, 2018e; IPBES, 2018c;
41 IPBES, 2018d; IPBES, 2019). Climate change and CO2 are expected to become increasingly important as
42 drivers of change over the coming decades (Ciais et al., 2013; Settele et al., 2014; IPBES, 2019; IPCC,
43 2019c).
44
45 2.1.2 Points of Departure
46
47 Species diversity and ecosystem function influence each other reciprocally, while ecosystem function forms
48 the necessary basis for ecosystem services (Hooper et al., 2012; Mokany et al., 2016). Drivers of impacts on
49 biodiversity, ecosystem function and ecosystem services have been assessed in reports from IPCC, Food and
50 Agriculture Organization (FAO), IPBES and the Global Environmental Outlook (Settele et al., 2014; FAO,
51 2018; IPBES, 2018b; IPBES, 2018e; IPBES, 2018c; IPBES, 2018d; IPBES, 2019; UNEP, 2019; Diversity,
52 2020). Most recently, the IPCC SRCCL provided an assessment on land degradation and desertification,
53 greenhouse gas emissions and food security in the context of global warming (IPCC, 2019c), and the IPBES-
54 IPCC joint report on Biodiversity and Climate Change provided a synthesis of current understanding of the
55 interactions, synergies and feedbacks between biodiversity and climate change (Pörtner et al., 2021). This
56 chapter builds on and expands the results from these assessments.
57
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1 Assessment of impacts of climate change on freshwater systems has been limited in previous assessments,
2 and interlinkages between terrestrial and freshwater processes have not been fully explored (Settele et al.,
3 2014; IPBES, 2019). Improved treatment of impacts on terrestrial and freshwater systems is critical
4 considering the revisions of international sustainability Goals and Targets, especially the conclusion that
5 many of the proposed post-2020 Biodiversity-targets of the Convention on Biological Diversity (CBD)
6 cannot be met due to climate change impacts (Arneth et al., 2020).
7
8 Previous reports highlighted the possibility of new ecosystem states stemming from shifts in thermal
9 regimes, species composition, and energy and matter flows (Settele et al., 2014; Shin et al., 2019). Projecting
10 such “tipping points” (see glossary) has been identified in previous reports as a challenge since neither
11 monitoring programmes nor field studies, nor ecosystem and biodiversity modelling tools capture the
12 underlying species-species and species-climate interactions sufficiently well to identify how biological
13 interactions within and across trophic levels may amplify or dampen shifts in ecosystem states (Settele et al.,
14 2014; Shin et al., 2019). Building on these previous analyses and recent literature, Chapter 2 in this AR6
15 provides new insights compared to previous assessments by (i) emphasising freshwater aspects, and the
16 interlinkages between freshwater and terrestrial systems, (ii) assessing more clearly the link between
17 biodiversity and ecosystem functioning, (iii) assessing impacts associated with climate change mitigation
18 scenarios versus impacts of climate change, including interactions with adaptation, and (iv) where possible,
19 places findings in context of the United Nations Sustainable Development Goals (SDGs) 2030, and services
20 for human societies.
21
22 2.1.3 Guide to Attribution and Traceability of Uncertainty Assessments
23
24 For biological systems we use the framework for detection and attribution outlined in AR5 in which
25 attribution of observed biological changes is made not to global, but to local or regional climate changes,
26 (Parmesan et al., 2013; Cramer et al., 2014). However, global distribution of regional responses is desirable
27 to achieve generality, and data in prior reports were concentrated from the northern hemisphere. The critique
28 of "global" studies by (Feeley et al., 2017) argues that their naming is misleading, that most of them are far
29 from global and that considerable geographic and taxonomic bias remains. This bias is diminishing, as data
30 from southern hemisphere regions are added and there is now representation from every continent.
31
32 Overall confidence in climate change attribution of a biological change can be increased in multiple ways
33 (Parmesan et al., 2013), of which we list four here. First, confidence rises when the time span of biological
34 records is long, such that decadal trends in climate can be compared with decadal trends in biological
35 response and long-term trends can be statistically distinguished from natural variability. Secondly,
36 confidence can be increased by examining a large geographic area, which tends to diminish the effects of
37 local confounding factors (Parmesan et al., 2013; Daskalova et al., 2020). Third, confidence is increased
38 when there is experimental or empirical evidence of a mechanistic link between particular climate metrics
39 and biological response. Fourth, confidence is increased when particular fingerprints of climate change are
40 documented that uniquely implicate climate change as the causal driver of the biological change (Parmesan
41 and Yohe, 2003). These conditions constitute multiple lines of evidence which, when they converge, can
42 provide very high confidence that climate change is the causal driver of an observed change in a particular
43 biological species or system (Parmesan et al., 2013).
44
45 Important factors that may confound or obscure effects of climate change are presence of invasive species,
46 changes in land use (LULCC), and, in freshwater systems, eutrophication (IPCC, 2019a). Temporal and
47 spatial scale of the study also affect estimates of impacts. The most extreme published estimates of
48 biological change tend to be derived from smaller areas and/or shorter timeframes (Daskalova et al., 2020),
49 and a recent large global analysis of data for 12,415 species found that differences in study methodology
50 accounted for most of the explained variance in reported range shifts (Lenoir et al., 2020). The importance of
51 land-use change is frequently stressed, but there is a paucity of studies that actually quantify the relative
52 effects of climate change and land-use change on species and communities. Sirami et al. (2017) found only
53 13 such studies, among which four concluded that effects of land-use change over-rode those of climate
54 change, four found that the two drivers independently affected different species and five found that they
55 acted in synergy.
56
57
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1 2.2 Connections of Ecosystem Services to Climate Change
2
3 Ecosystems provide services essential for human survival and well-being. The Millennium Ecosystem
4 Assessment defined ecosystem services as “the benefits people obtain from ecosystems” including
5 “provisioning services such as food and water; regulating services such as regulation of floods, drought, land
6 degradation, and disease; supporting services such as soil formation and nutrient cycling; and cultural
7 services such as recreational, spiritual, religious, and other nonmaterial benefits” (Assessment, 2005).
8
9 The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) re-named
10 the concept “nature’s contributions to people” and broadened the definition to “the contributions, both
11 positive and negative, of living nature (i.e. diversity of organisms, ecosystems, and their associated
12 ecological and evolutionary processes) to the quality of life for people. Beneficial contributions from nature
13 include such things as food provision, water purification, flood control, and artistic inspiration, whereas
14 detrimental contributions include disease transmission and predation that damages people or their assets”
15 (IPBES, 2019). IPBES modified the concept to include more social viewpoints and broaden analyses beyond
16 narrow economic stock-and-flow valuation approaches (Díaz et al., 2018). IPBES developed a classification
17 of 18 categories of ecosystem services (see Table 2.1).
18
19 When anthropogenic climate change affects ecosystems, it can also affect ecosystem services for people.
20 Climate change connects to ecosystem services through three links: climate change—species—ecosystems—
21 ecosystem services. This IPCC chapter assesses these connections through all three links when end-to-end
22 published scientific analyses are available for terrestrial and freshwater ecosystems. This type of robust
23 evidence exists for some key ecosystem services (Section 2.5.4), and is assessed in specific report sections:
24 biodiversity habitat creation and maintenance (Sections 2.4, 2.5), regulation of detrimental organisms and
25 biological processes (Sections 2.4.2.3, 2.4.2.7, 2.4.4, 2.5.3, 2.6.4, Cross-Chapter Box ILLNESS this
26 Chapter), regulation of climate through ecosystem feedbacks in terms of carbon storage (Sections 2.4.4.4,
27 2.5.2.10, 2.5.3.4, 2.5.3.5) and albedo (Section 2.5.3.5), and provision of freshwater from ecosystems to
28 people (Section 2.5.3.6).
29
30 For ecosystem services that do not have published scientific information to establish unambiguous links to
31 climate change, the climate—species—ecosystem links are assessed. Global ecological assessments,
32 including the Global Biodiversity Assessment (Programme, 1995), the Millennium Ecosystem Assessment
33 (Assessment, 2005), and the IPBES Global Assessment Report (IPBES, 2019) have synthesised scientific
34 information on the ecosystem—ecosystem services link, but full assessment from climate change to
35 ecosystem services is often impeded by limited quantitative studies that span this entire spectrum, see
36 (Mengist et al., 2020) for a review of this gap in montane regions.
37
38 IPCC and IPBES are collaborating to address gaps in knowledge on the effects of climate change on
39 ecosystem services (Services and Ecosystem, 2021). Table 2.1 provides a guide for finding information on
40 climate change and individual ecosystem services in the IPCC Sixth Assessment Report.
41
42
43 Table 2.1: Connections of ecosystem services to climate change, indicating the 18 categories of nature’s contributions
44 to people of the IPBES (IPBES, 2019), the most relevant sections in the IPCC Sixth Assessment Report, and the level
45 of evidence in this report for attribution to anthropogenic climate change of observed impacts on ecosystem services.
46 The order of services in the table follows the order presented by IPBES and does not denote importance or priority.
47 Connections denote observed impacts, future risks, and adaptation. The order of connections follows the relevance or
48 the order of sections.
Ecosystem service Connections to climate change
Habitat creation and Species extinctions (2.4.2.2, 2.5.1.3), Species range shifts (2.4.2.1, 2.4.2.5),
maintenance Ecological changes in freshwater ecosystems (2.3.3, 2.4.2.3.2, 2.4.4.1,
2.4.4.5.2, 2.5.1.3.2, 2.5.3.5, 2.5.4, 2.5.3.6, 2.5.5.8), Vegetation changes (2.4.3,
2.4.4.2.5, 2.4.4.3, 2.4.4.4, 2.4.4.5.1, 2.5.2, 2.5.3.3), Biome shifts (2.4.3.2,
2.5.4), Wildfire (2.4.4.2, 2.5.3.2), Tree mortality (2.4.4.3, 2.5.3.3)
(robust evidence)
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Pollination and dispersal of seeds Species extinctions (2.4.2.2, 2.5.1.3), Species range shifts (2.4.2.1, 2.4.2.5),
and other propagules Phenology changes (2.4.2.4, 2.4.2.5)
(medium evidence)
Regulation of air quality Wildfire (2.4.4.2, 2.5.3.2, Chapter 7), Tree mortality (2.4.4.3, 2.5.3.3)
(medium evidence)
Regulation of climate Ecosystem carbon stocks, emissions, and removals (2.4.4.4, 2.5.3.4, IPCC AR6
Working Group I, Chapter 5), Amazon rainforest dieback (2.4.3.6, 2.4.4.3.2,
2.4.4.4.2, 2.5.2.6, 2.5.2.10, 2.5.3.3), Tundra permafrost thaw (2.4.4.4.4, 2.5.2.8,
2.5.3.5, 2.5.4), biome shifts (2.4.3, 2.5.2, 2.5.3.2.2), Wildfire (2.4.4.2, 2.5.3.2),
Tree mortality (2.4.4.3, 2.5.3.3), Primary productivity changes (2.4.4.5, 2.5.3.5)
(robust evidence)
Regulation of ocean acidification Ocean acidification (IPCC AR6 Working Group I, Chapter 5), Changes in
marine species distribution and abundance (Chapter 3)
(robust evidence)
Regulation of freshwater Physical changes in freshwater systems (2.3.3), Ecological changes in
quantity, location, and timing freshwater ecosystems (2.4.2.3.2, 2.4.4.1, 2.4.4.5.2, 2.5.1.3.2, 2.5.3.7), Tree
mortality (2.4.4.3, 2.5.3.3), Freshwater supply from ecosystems (2.5.3.6)
(medium evidence)
Regulation of freshwater and Coastal ecosystem changes (Chapter 3), Physical changes in freshwater systems
coastal water quality (2.3.3), Ecological changes in freshwater ecosystems (2.4.2.3.2; 2.4.4.1,
2.4.4.5.2, 2.5.1.3.2, 2.5.3.7)
(robust evidence)
Formation, protection, and Agricultural ecosystem changes (Chapter 5), Physical changes in freshwater
decontamination of soils and systems (2.3.1), Vegetation changes (2.4.3, 2.5.4), Wildfire (2.4.4.2, 2.5.3.2)
sediments (medium evidence)
Regulation of hazards and Coastal ecosystem changes (Chapter 3), Vegetation changes (2.4.3, 2.5.2),
extreme events Wildfire (2.4.4.2, 2.5.5.2), Summary of hazards (2.3), Cross-Chapter Box
EXTREMES this Chapter
(medium evidence)
Regulation of detrimental Inter-species interactions (2.4.2), Control of disease vectors (2.4.2.7, 2.5.1,
organisms and biological 2.6.4), Insect pest infestations (2.4.4.3), Cross-Chapter Box ILLNESS this
processes Chapter
(medium evidence)
Energy Forestry plantation changes (Chapter 5), Biomass changes in natural
ecosystems (2.4.4.4), Bioeconomy (Cross-Working Group Box
BIOECONOMY in Chapter 5), Tree mortality (2.4.4.3, 2.5.3.3)
(limited evidence)
Food and feed Agricultural ecosystem changes (Chapter 5), Species extinctions (2.4.2.2,
2.5.1.3), Species range shifts (2.4.2.1), Nature-based services from natural
ecosystems (Cross-Chapter Box NATURAL this chapter), shifts in commercial
food species Cross-Chapter Box Moving Plate in Chapter 5)
(medium evidence)
Materials, companionship, and Forestry plantation changes (Chapter 5), Species extinctions (2.4.2.2, 2.5.1.3),
labour Species range shifts (2.4.2.1), Tree mortality (2.4.4.3, 2.5.3.3)
(limited evidence)
Medicinal, biochemical, and Species extinctions (2.4.2.2, 2.5.1.3), Species range shifts (2.4.2.1)
genetic resources (limited evidence)
Learning and inspiration All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater
ecosystems
(limited evidence)
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FINAL DRAFT Chapter 2 IPCC WGII Sixth Assessment Report
Physical and psychological All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater
experiences ecosystems
(limited evidence)
Supporting identities All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater
ecosystems
(limited evidence)
Maintenance of options All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater
ecosystems, Nature-based services from natural ecosystems (Cross-Chapter
Box NATURAL this Chapter, Cross-Chapter Box DEEP in Chapter 17
(limited evidence)
1
2
3 2.3 Hazards and Exposure
4
5 In AR6, WGI describes changes in physical climate systems using the term ‘climatic impact-drivers’ (CIDs),
6 which can have detrimental, beneficial or neutral effects on a system. In contrast, the literature on natural
7 systems tends to focus on hazards, which include natural or human-induced physical events, impacts or
8 trends with the potential to cause negative effects on ecosystems and environmental resources. Hazards are
9 affected by current and future changes in climate, including altered climate variability and extreme events
10 (WGI Chapter 12). Hazards can occur suddenly (e.g., a heat wave or heavy rain event), or more slowly (e.g.,
11 land loss, degradation and erosion linked to multiple climate hazards compounding). Observed exposure and
12 risks to protected areas is assessed in Section 2.5.3.1.1. See also Cross-Chapter Box EXTREMES this
13 Chapter.
14
15 Non-climatic hazards such as land use change, habitat fragmentation, pollution and invasive species have
16 been the primary drivers of change in terrestrial and freshwater ecosystems in the past (high confidence)
17 (Figure 2.1). These impacts have been extensively documented in reports by the Intergovernmental Science-
18 Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2021). However, whilst climate change has
19 not been the predominant influence to date, its relative impact is increasing (IPCC Special Report on Climate
20 Change and Land (SRCCL)), with greater interactive effects of non-climate and climate hazards now
21 occurring (Birk et al., 2020).
22
23 2.3.1 Observed Changes to Hazards and Extreme Events
24
25 The major climate hazards at the global level are generally well understood (WGI AR6 Chapter 12; WGI
26 AR6 Interactive Atlas). Increased temperatures and changes to rainfall and runoff patterns; greater variability
27 in temperature, rainfall, river flow and water levels; rising sea-levels and increased frequency of extreme
28 events means that greater areas of the world are being exposed to climate hazards outside those to which
29 they are adapted (high confidence) (Lange et al., 2020).
30
31 Extreme events are a natural and important part of many ecosystems and many organisms have adapted to
32 cope with long-term and short-term climate variability, within the disturbance regime experienced during
33 their evolutionary history (high confidence). However, climate changes, disturbance regimes change and the
34 magnitude and frequency of extreme events such as floods, droughts, cyclones, heatwaves and fire have
35 increased in many regions (high confidence). These disturbances affect ecosystem functioning, biodiversity
36 and ecosystem services (high confidence) but are, in general, poorly captured in impact models (Albrich et
37 al., 2020b), although this should improve as higher-resolution climate models that better capture smaller-
38 scale processes and extreme events become available (WGI AR6, Chapter 11). Extreme events pose large
39 challenges for Ecosystem-based Adaptation (IPCC Special Reports on Extremes, Section 2.6.3). Ecosystem
40 functionality on which such adaptation measures rely may be altered or destroyed by extreme episodic
41 events (Handmer et al., 2012; Lal et al., 2012; Pol et al., 2017).
42
43 There is high confidence that the combination of internal variability, superimposed on longer-term climate
44 trends, is pushing ecosystems to tipping points, beyond which abrupt and possibly irreversible changes are
45 occurring (Harris et al., 2018a; Jones et al., 2018; Hoffmann et al., 2019b; Prober et al., 2019; Berdugo et al.,
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FINAL DRAFT Chapter 2 IPCC WGII Sixth Assessment Report
1 2020; Bergstrom et al., 2021). Increases in the frequency and severity of heatwaves, droughts and aridity,
2 floods, fires and extreme storms have been observed in many regions (Seneviratne et al., 2012; Ummenhofer
3 and Meehl, 2017) and these trends are projected to continue (high confidence) (Section 3.2.2.1, Cross-
4 Chapter Box EXTREMES this Chapter, AR6 WGI Chapter 11;, SR1.5, Hoegh-Guldberg et al., 2018b).
5
6 While the major climate hazards at the global level are generally well described with high confidence, there
7 is less understanding about the importance of hazards on ecosystems when they are superimposed (Allen et
8 al., 2010; Anderegg et al., 2015; Seidl et al., 2017; Dean et al., 2018), and the outcomes are difficult to
9 quantify in future projections (Handmer et al., 2012). Simultaneous or sequential events (coincident or
10 compounding events) can lead to an extreme event or impact, even if each event is not in themselves extreme
11 (Denny et al., 2009; Hinojosa et al., 2019). For example, the compounding effects of sea-level rise, extreme
12 coastal high tide, storm surge, and river flow can substantially increase flooding hazard and impacts on
13 freshwater systems (Moftakhari et al., 2017). On land, changing rainfall patterns and repeated heat waves
14 may interact with biological factors such as altered plant growth and nutrient allocation under elevated CO2,
15 affecting herbivory rates and insect outbreaks leading to widespread dieback of some forests (e.g. in
16 Australian Eucalypt forests) (Gherlenda et al., 2016; Hoffmann et al., 2019a). Risk assessments typically
17 only consider a single climate hazard without changing variability, potentially underestimating actual risk (
18 Milly et al., 2008; Sadegh et al., 2018; Zscheischler et al., 2018; Terzi et al., 2019; Stockwell et al., 2020; ).
19
20 Understanding impacts associated with the rapid rate of climate change is less developed and more uncertain
21 than changes in mean climate. High climate velocity (Loarie et al., 2009) is expected to be associated with
22 distribution shifts, incomplete range filling and species extinctions (high confidence) (Sandel et al., 2011;
23 Burrows et al., 2014), although not all species are equally at risk from high velocity (see Sections 2.4.2.2,
24 2.5.1.3). It is generally assumed that the more rapid the rate of change, the greater the impact on species and
25 ecosystems, but responses are taxonomically and geographically variable (high confidence) (Kling et al.,
26 2020).
27
28 For example, strong dispersers are less at risk, while species with low dispersal ability, small ranges and long
29 lifespans (e.g. many plants, especially trees, many amphibians and some small mammals) are more at risk
30 (Hamann et al., 2015) (IPCC, 2014). This is likely to favour generalist and invasive species, altering species
31 composition, ecosystem structure and function (Clavel et al., 2011; Büchi and Vuilleumier, 2014). The
32 ability to track suitable climates is substantially reduced by habitat fragmentation and human modifications
33 of the landscape such as dams on rivers and urbanisation (high confidence). Freshwater systems are
34 particularly at risk of rapid warming given their naturally fragmented distribution. Velocity of change in
35 surface temperature of inland standing waters globally has been estimated as 3.5 ± 2.3 km per decade from
36 1861 to 2005. This is projected to increase from 2006 to 2099 from between 8.7 ± 5.5 km per decade (RCP
37 2.6) to 57.0 ± 17.0 km per decade (RCP 8.5) (Woolway and Maberly, 2020). Although the dispersal of aerial
38 adult stage of some aquatic insects can surpass these climate velocities, rates of change under mid- and high
39 emissions scenarios (RCP4.5, RCP6.0, RCP8.5) are substantially higher than known rates of active dispersal
40 of many species (Woolway and Maberly, 2020). Many species, both terrestrial and freshwater, are not
41 expected to be able to disperse fast enough to track suitable climates under mid- and high emissions
42 scenarios (medium confidence) (RCP4.5, RCP6.0, RCP8.5; Brito-Morales et al., 2018).
43
44
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1
2 Figure 2.1: Map of global land use change from 1982-2016. Based on satellite records of global tree canopy (TC),
3 short vegetation (SV) and bare ground (BG) cover (from Song et al., 2018). a) Mean annual estimates of cover (% of
4 pixel area at 0.05° resolution). b) Long-term change estimates (% of pixel area at 1.5° resolution), with pixels showing
5 a statistically significant trend (n = 35 years, two-sided Mann–Kendall test, P < 0.05) in TC, SV or BG. The dominant
6 changes are Tree canopy gain with Short vegetation loss; Bare Ground gain with Short vegetation loss; Tree canopy
7 gain with Bare Ground loss; Bare Ground gain with Tree canopy loss; 5, Short vegetation gain with Bare Ground loss;
8 and Short vegetation gain with TC loss. Grey indicates areas with no significant change between 1982-2016.
9
10
11 2.3.2 Projected Impacts of Extreme Events
12
13 Understanding of the large-scale drivers and the local to regional feedback processes that lead to extreme
14 events is still limited and projections of extremes and coincident or compounding events remain uncertain
15 (Prudhomme et al., 2014; Sillmann et al., 2017; Hao et al., 2018; Miralles et al., 2019). Extreme events are
16 challenging to model because they are by definition rare and often occur at spatial and temporal scales much
17 finer than the resolution of climate models (Sillmann et al., 2017; Zscheischler et al., 2018). Additionally,
18 the processes that cause extreme events often interact, as is the case for drought and heat events, and are
19 spatially and temporally dependent, for example, as is the case in soil moisture and temperature (Vogel et al.,
20 2017). Understanding feedbacks between land and atmosphere also remains limited. For example, positive
21 feedbacks between soil and vegetation, or between evaporation, radiation and precipitation are important in
22 the preconditioning of extreme events such as heatwaves and droughts, increasing the severity and impact of
23 extreme events (Miralles et al., 2019).
24
25 Despite recent improvements in observational studies and climate modelling (Santanello et al., 2015;
26 Stegehuis et al., 2015; PaiMazumder and Done, 2016; Basara and Christian, 2018; Knelman et al., 2019), the
27 potential to quantify or infer formal causal relationships between multiple drivers and/or hazards remains
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1 limited, for several reasons (Zscheischler and Seneviratne, 2017; Kleinman et al., 2019; Miralles et al., 2019;
2 Yokohata et al., 2019; Harris et al., 2020). The mechanisms underlying the response are difficult to identify
3 (e.g., response to heat stress, drought, insects), effects vary among species and at different life stages, and an
4 initial stress may influence the response to further stress (Nolet and Kneeshaw, 2018). Additionally, hazards
5 such as drought are often exacerbated by societal, industrial and agricultural water demands, requiring more
6 sophisticated modelling of the physical and human systems (Mehran et al., 2017; Wan et al., 2017).
7 Observations of past compound events may not provide reliable guides to how future events may evolve,
8 because human activity and recent climate change continue to interact to influence both system functioning
9 and the climate state that have not been experienced in the past (see Chapter 11, WGI AR6).
10
11 2.3.3 Biologically Important Physical Changes in Freshwater Systems
12
13 Physical changes are fundamental drivers of change at all levels of biological organisation, from individual
14 species to communities to whole ecosystems and climate hazards specific to freshwater systems which are
15 not documented elsewhere in AR6 are summarised here.
16
17 2.3.3.1 Observed Change in Thermal Habitat and Oxygen Availability
18
19 Since AR5, evidence for changes in temperatures of lakes and rivers has continued to increase. Global
20 warming rates for lake surface waters were estimated as 0.21°C to 0.45°C per decade between 1970-2010,
21 exceeding SST trends of 0.09°C per decade between 1980-2017 (robust evidence, high agreement) (Figure
22 2.2; Schneider and Hook, 2010; Kraemer et al., 2015; O'Reilly et al., 2015; Woolway et al., 2020b).
23 Warming of lake surface water temperatures was variable within regions (O'Reilly et al., 2015) but more
24 homogeneous than changes of deep water temperature (Pilla et al., 2020). Because temperature trends in
25 lakes can vary vertically, horizontally, and seasonally, complex changes have occurred in the amounts of
26 habitat available to aquatic organisms at particular depths and temperatures (Kraemer et al., 2021).
27
28 Changes in river water temperatures ranged from -1.21 to +1.076 °C per decade between 1901-2010
29 (medium evidence, medium agreement) (Figure 2.2; Hari et al., 2006; Kaushal et al., 2010; Jurgelėnaitė et al.,
30 2012; Li et al., 2012; Latkovska and Apsīte, 2016; Marszelewski and Pius, 2016). The more rapid increase in
31 surface water temperature in lakes and rivers in regions with cold winters (O'Reilly et al., 2015) can in part
32 be attributed to the amplified warming in polar and high latitude regions (robust evidence, high agreement)
33 (Figure 2.2b; Screen and Simmonds, 2010; Stuecker et al., 2018).
34
35 Shifts in thermal regime: Since AR5 the trend that lake waters mix less frequently continues (Butcher et al.,
36 2015; Adrian et al., 2016; Richardson et al., 2017; Woolway et al., 2017). This results from greater warming
37 of surface temperatures relative to deep water temperatures and the loss of ice during winter which prevents
38 inverse thermal stratification in north temperate lakes (robust evidence, high agreement) (Adrian et al., 2009;
39 Winslow et al., 2015; Adrian et al., 2016; Schwefel et al., 2016; Richardson et al., 2017).
40
41 Oxygen availability: Increased water temperature and reduced mixing cause a decrease in dissolved oxygen.
42 In 400 lakes, dissolved oxygen in surface and deep waters declined by 4.1 and 16.8%, respectively between
43 1980 and 2017 (Jane et al., 2021). The deepest water layers are expected to experience an increase in
44 hypoxic conditions by more than 25% due to reduced winter mixing and fewer complete mixing events, with
45 strong repercussions on nutrient dynamics and loss of thermal habitat (robust evidence, high agreement)
46 (Straile et al., 2010; Zhang et al., 2015; Schwefel et al., 2016; Adrian et al., 2016; Kraemer et al., 2021).
47
48
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1
2 Figure 2.2: Observed global trends in lake and river surface water temperature. a) Left panel: Map of
3 temperatures of lakes (1970-2010). b) Left panel: Map of temperatures of rivers (1901-2010). Note that the trends of
4 river water temperatures are not directly comparable within rivers or directly comparable to lakes since time periods are
5 not consistent across river studies. Right panels in a) and b) depict water temperature trends along a latitudinal gradient
6 highlighting the above average warming rates in northern Polar Regions (polar amplification). Data sources for lakes:
7 (O'Reilly et al., 2015; Carrea and Merchant, 2019; Woolway et al., 2020a; Woolway et al., 2020b). Data sources for
8 rivers: (Webb and Walling, 1992; Langan et al., 2001; Daufresne et al., 2004; Moatar and Gailhard, 2006; Lammers et
9 al., 2007; Patterson et al., 2007; Webb and Nobilis, 2007; Durance and Ormerod, 2009; Kaushal et al., 2010; Pekárová
10 et al., 2011; Jurgelėnaitė et al., 2012; Markovic et al., 2013; Arora et al., 2016; Latkovska and Apsīte, 2016;
11 Marszelewski and Pius, 2016; Jurgelėnaitė et al., 2017).
12
13
14 2.3.3.2 Observed Changes in Water Level
15
16 Depending on how the intensification of the global water cycle affects individual lake water budgets, the
17 amount of water stored in specific lakes may increase, decrease, or have no substantial cumulative effect
18 (Notaro et al., 2015; Pekel et al., 2016; Rodell et al., 2018; Busker et al., 2019; Woolway et al., 2020b). The
19 magnitude of hydrological changes that can be assuredly attributed to climate change remains uncertain
20 (Hegerl et al., 2015; Gronewold and Rood, 2019; Kraemer et al., 2020). Attribution of water storage
21 variation in lakes due to climate change is facilitated when such variations occur coherently across broad
22 geographic regions and long timescales, preferably absent other anthropogenic hydrological influences
23 (Watras et al., 2014; Kraemer et al., 2020). There is increasing awareness that climate change contributes to
24 the loss of small temporary ponds, which cover a greater global area than lakes (Bagella et al., 2016).
25
26 Lakes fed by glacial meltwater are growing in response to climate change and glacier retreat (robust
27 evidence, high agreement) (Shugar et al., 2020). Water storage increases on the Tibetan Plateau (Figure 2.3a)
28 have been attributed to changes in glacier melt, permafrost thaw, precipitation and runoff, in part as a result
29 of climate change (Huang et al., 2011; Meng et al., 2019; Wang et al., 2020a). High confidence in attribution
30 of these trends to climate change is supported by long-term ground survey data and observations from the
31 GRACE satellite mission (Ma et al., 2010; Rodell et al., 2018; Kraemer et al., 2020).
32
33 In the Arctic, lake area has increased in regions with continuous permafrost and decreased in regions where
34 permafrost is thinner and discontinuous (robust evidence, high agreement) (see Chapter 4; Smith et al., 2005;
35 Andresen and Lougheed, 2015; Nitze et al., 2018; Mekonnen et al., 2021).
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1
2
3
4 Figure 2.3: Change in water extent in the Tibetan Plateau and annual mean global river flow. a) Changes in
5 water storage on the Tibetan Plateau. Map of the Qinghai-Tibetan Plateau, Asia showing the percent change in surface
6 water extent from 1984-2019 based on LANDSAT imagery. Increases in surface water extent in this region are mainly
7 caused by climate-change mediated increases in precipitation and glacial melt (Source: EC JRC/Google; (Pekel et al.,
8 2016). b) Global map of the median trend in annual mean river flow derived from 7250 observatories around the world
9 (period 1971-2010). Some regions are drying (northeast Brazil, southern Australia, and the Mediterranean) and others
10 are wetting (northern Europe) mainly caused by large-scale shifts in precipitation, changes in factors that influence
11 evapotranspiration and alterations of the timing of snow accumulation and melt driven by rising temperatures (Source:
12 Gudmundsson et al., 2021).
13
14
15 2.3.3.3 Observed Changes in Discharge
16
17 Analysis of river flows from 7,250 observatories around the world covering the years 1971 to 2010 and
18 identified spatially complex patterns, with reductions in northeastern Brazil, southern Australia and the
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1 Mediterranean and increases in northern Europe (medium evidence, medium agreement) (Figure 2.3b;
2 Gudmundsson et al., 2021). More than half of global rivers undergo periodic drying that reduces river
3 connectivity (medium evidence, medium agreement). Increased frequency and intensity of droughts may
4 cause perennial rivers to become intermittent and intermittent rivers to disappear (medium evidence, medium
5 agreement), threatening freshwater fish in habitats already characterised by heat and droughts (Datry et al.,
6 2016; Schneider et al., 2017; Jaric et al., 2019). In high altitude/latitude streams, reduced glacier and
7 snowpack extent, earlier snowmelt and altered precipitation patterns, attributed to climate change, have
8 increased flow intermittency (Vorosmarty et al., 2010; Siebers et al., 2019; Gudmundsson et al., 2021)
9 Patterns in flow regimes can be directly linked to a variety of processes shaping freshwater biodiversity,
10 hence any climate-change induced changes on flow regimes and river connectivity are expected to alter
11 species composition, as well as having societal impacts (See Chapter 3 of IPCC SR1.5; Bunn and
12 Arthington, 2002; Thomson et al., 2012; Chessman, 2015; Kakouei et al., 2018)
13
14 2.3.3.4 Observed Loss of Ice
15
16 Studies since AR5 have confirmed ongoing and accelerating loss of lake and river ice in the northern
17 hemisphere (robust evidence, high agreement) (Figure 2.4). In recent decades, systems have been freezing
18 later in winter and thawing earlier in spring, reducing ice duration by >2 weeks per year and leading to
19 increasing numbers of years with loss of perennial ice cover, leading to intermittent ice-cover or even
20 absence of ice (Adrian et al., 2009; Kirillin et al., 2012; Paquette et al., 2015; Adrian et al., 2016; Park et al.,
21 2016; Roberts et al., 2017; Sharma et al., 2019). The global extent of river ice declined by 25% between
22 1984 and 2018 (Yang et al., 2020). This trend has been more pronounced at higher latitudes, consistent with
23 enhanced polar warming (large geographic coverage) (Du et al., 2017). Empirical long-term and remote
24 sensing data gathered in an increasingly large number of freshwater systems supports very high confidence in
25 attribution of these trends to climate change. For declines of glaciers, snow and permafrost see AR6 WGII
26 Chapter 4 and SROCC report.
27
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1
2 Figure 2.4: Global ice cover trends of lakes and rivers. a) Spatial distribution of current (light grey areas) and future
3 (coloured areas) Northern Hemisphere lakes that may experience intermittent winter ice cover with climate warming.
4 Projections were based on current conditions (1970-2010) and four established air temperature projections (Data
5 source: (Sharma et al., 2019). b) Spatial distribution of projected change in Northern Hemisphere river ice duration
6 under the RCP 4.5 emission scenario by 2080-2100 relative to the period 2009-2029. White areas refer to rivers without
7 ice cover in the period 2009-2029 (zero days). Reference period isolines indicate river ice duration in the period 2009-
8 2029. Coloured areas depict loss of ice duration in days. Blue areas depict a projected increase in river ice duration.
9 Grey land areas indicate a lack of Landsat-observable rivers (Data source: Yang et al., 2020).
10
11
12 2.3.3.5 Extreme Weather Events and Freshwater Systems
13
14 Since AR5, numerous drastic short-term responses have been observed in lakes and rivers, both to expected
15 seasonal extreme events and to unexpected supra-seasonal extremes extending over multiple seasons.
16 Consequences for ecosystem functioning are not well understood (Bogan et al., 2015; Death et al., 2015;
17 Stockwell et al., 2020). Increasing frequencies of severe floods and droughts attributed to climate change are
18 major threats for river ecosystems (Peters et al., 2016; Alfieri et al., 2017). While extreme floods cause
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1 massive physical disturbance, moderate floods can have positive effects, providing woody debris that
2 contributes to habitat complexity and diversity, flush fine sediments, dissolving organic carbon and
3 providing important food sources from terrestrial origins (Peters et al., 2016; Talbot et al., 2018). Droughts
4 reduce river habitat diversity and connectivity, threatening aquatic species, especially in deserts and arid
5 regions (Bogan et al., 2015; Death et al., 2015; Ledger and Milner, 2015; Jaric et al., 2019).
6
7 Rivers already stressed by human activities such as urban development and farming on floodplains are prone
8 to reduced resilience to future extreme events (medium confidence) (Woodward et al., 2016; Talbot et al.,
9 2018). Thus, potential for floods to become catastrophic for ecosystem services are exacerbated by land-use
10 changes (Peters et al., 2016; Talbot et al., 2018). However, biota can recover rapidly from extreme flood
11 events if river geomorphology is not reformed. If instream habitat is strongly affected, recovery, if it occurs,
12 takes much longer, resulting in decline in biodiversity (medium confidence) (Thorp et al., 2010; Death et al.,
13 2015; Poff et al., 2018).
14
15 However, not all extreme events will have a biological impact, depending in particular on the timing,
16 magnitude, frequency of events and the antecedent conditions (Bailey and van de Pol, 2016; Stockwell et al.,
17 2020; Jennings et al., 2021; Thayne et al., 2021). For instance, an extreme wind event may have little impact
18 on phytoplankton in a lake, which was fully mixed prior to the event. Conversely, storm effects on
19 phytoplankton communities may compound when lakes are not yet recovered from a previous storm or if
20 periods of drought alternate with periods of intense precipitation (limited evidence) (Leonard, 2014;
21 Stockwell et al., 2020).
22
23 In summary, extreme events (heat waves, storms, loss of ice) affect lakes in terms of water temperature,
24 water level, light, oxygen concentrations and nutrient dynamics, that in turn affect primary production, fish
25 communities and greenhouse gas emissions (high confidence). These impacts are modified by levels of solar
26 radiation, wind speed and precipitation (Woolway et al., 2020a). Droughts have negative impact on water
27 quality in streams and lakes by increasing water temperature, salinity, the frequency of algal blooms and
28 contaminant concentrations, and reducing concentrations of nutrients and dissolved oxygen (medium
29 confidence) (Peters et al., 2016; Alfieri et al., 2017) (Woolway et al., 2020a). Understanding how these
30 pressures subsequently cascade through freshwater ecosystems will be essential for future projections of
31 their resistance and resilience towards extreme events (Leonard, 2014; Stockwell et al., 2020). See Table
32 SM2.1 for specific examples of observed changes.
33
34 2.3.3.6 Projected Changes in Physical Characteristics of Lakes and Rivers
35
36 Given the strength of relationship between past GAST and warming trends at lake surfaces (Figure 2.2;
37 section 2.3.3.1), and projected increases in heatwaves, surface water temperatures are projected to continue
38 to increase (Woolway et al., 2021). Mean May to October lake surface temperatures in 46,557 European
39 lakes were projected to be 2.9°, 4.5°, and 6.5°C warmer by 2081-2099 compared to historic (1981-1999)
40 under RCPs 2.0, 6.0, and 8.5, respectively (Woolway et al., 2020a). Under RCP 2.6, average lake heatwave
41 intensity increases from 3.7° to 4.0°C and average duration from 7.7 to 27.0 days, relative to the historical
42 period (1970-1999). For RCP 8.5, warming increases to 5.4°C and duration increases dramatically to 95.5
43 days (medium confidence) (Woolway et al., 2021).
44
45 Worldwide alteration of lake mixing regimes in response to climate change are projected (Kirillin, 2010).
46 Most prominently, monomictic lakes–undergoing one mixing event in most years–will become permanently
47 stratified, while lakes that are currently dimictic–mixing twice per year–will become monomictic by 2080-
48 2100 (medium confidence) (Woolway and Merchant, 2019). Nevertheless, predicting mixing behavior
49 remains an important challenge and attribution to climate change remains difficult (Schwefel et al., 2016;
50 Bruce et al., 2018).
51
52 Under climate projections of 3.2°C warming, 4.6% of the ice covered lakes in the northern hemisphere could
53 switch to intermittent winter ice cover (Figure 2.4a; Sharma et al., 2019). Unfrozen and warmer lakes lose
54 more water to evaporation (Wang et al., 2018b). By 2100, global annual lake evaporation will increase by
55 16%, relative to 2006-2015, under RCP 8.5 (Woolway et al., 2020b). Moreover, melting of ice decreases the
56 ratio of sensible to latent heat flux, thus channelling more energy into evaporation (medium confidence)
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1 (Wang et al., 2018b). Between 2009-2029 and 2080-2100, average river ice duration is projected to decline
2 by 7.3 and 16.7 days under the RCP 4.5 and RCP 8.5 (Figure 2.4b; Yang et al., 2020).
3
4 Projections of lake water are limited by the absence of reliable, long-term, homogenous, and spatially
5 resolved hydrologic observations (Hegerl et al., 2015). This uncertainty is reflected in the widely divergent
6 projections for lake water storage in response to future climate changes in individual lakes (Angel and
7 Kunkel, 2010; Malsy et al., 2012) (MacKay and Seglenieks, 2012; Notaro et al., 2015). Selecting models that
8 perform well when comparing hindcasted to observed past water storage variation often does little to reduce
9 water storage projection uncertainty (Angel and Kunkel, 2010). This wide range of potential changes
10 complicates lake management. For information on observed and projected changes in the global water cycle
11 and hydrological regimes for streams, lakes, wetland, groundwater and their implications on water quality
12 and societies, see Chapter 4 of WGII and Chapter 8 of WGI. For the role of weather and climate extremes on
13 the global water cycle, see Chapter 11 of WGI.
14
15 In summary, with ongoing climate warming and an increase in the frequency and intensity of extreme
16 events, observed increases in water temperature, losses of ice and shifts in thermal regime, are projected to
17 continue (high confidence).
18
19
20 [START CROSS-CHAPTER BOX EXTREMES HERE]
21
22 Cross-Chapter Box EXTREMES: Ramifications of Climatic Extremes for Marine, Terrestrial,
23 Freshwater and Polar Natural Systems
24
25 Authors: Rebecca Harris (Australia, Chapter 2, CCP3), Philip Boyd (Australia, Chapter 3), Rita Adrian
26 (Germany, Chapter 2), Jörn Birkmann (Germany, Chapter 8), Sarah Cooley (USA, Chapter 3), Simon
27 Donner (Canada, Chapter 3), Mette Mauritzen (Norway, Chapter 3), Guy Midgley (South Africa, Chapter
28 16); Camille Parmesan (France/USA/United Kingdom, Chapter 2), Dieter Piepenburg (Germany, Chapter
29 13, CCP6), Marie-Fanny Racault (United Kingdom/France, Chapter 3), Björn Rost (Germany, Chapter 3,
30 CCP6), David Schoeman (Australia, Chapter 3), Maarten van Aalst (The Netherlands, Chapter 16).
31
32 Introduction
33
34 Extreme events are now causing profound negative effects across all realms of the world (marine, terrestrial,
35 freshwater and polar) (medium confidence) (WGI, Chapter 9, 11; WGII AR6 Section 2.3.1, 2.3.2, 2.3.3.5,
36 Chapter 3, Chapters 9–12). Changes to population abundance, species distributions, local extirpations and
37 extinctions are leading to long-term, potentially irreversible shifts in the composition, structure and function
38 of natural systems (medium confidence) (Frolicher and Laufkotter, 2018; Harris et al., 2018a; Maxwell et al.,
39 2019; Smale et al., 2019). These effects have widespread ramifications for ecosystems and the services they
40 provide – physical habitat, erosion control, carbon storage, nutrient cycling and water quality, with knock-on
41 effects on tourism, fisheries, forestry and other natural resources (Kaushal et al., 2018; Heinze et al., 2021;
42 Pörtner et al., 2021).
43
44 Increasingly, the magnitude of extreme events is exceeding values projected for mean conditions for 2100,
45 regardless of emissions scenario (Figure Cross-Chapter Box EXTREMES.1). This has collapsed the timeline
46 organisms and natural communities have to acclimate or adapt to climate change (medium confidence).
47 Consequently, rather than having decades to identify, develop and adopt solutions, there is now an urgent
48 need to build resilience and assist recovery following extreme events.
49
50 Recent extremes highlight characteristics that enable natural systems to resist or recover from events, helping
51 natural resource managers to develop solutions to improve resilience of natural communities and identify
52 limits to adaptation (Bergstrom et al., 2021).
53
54 Marine Heatwaves
55
56 Consensus is emerging that anthropogenic climate change has significantly increased the likelihood of recent
57 marine heat waves (MHWs) (medium confidence) (WGI AR6, Chapter 9; Oliver et al., 2018). A widespread
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1 MHW occurred in the NE Pacific between 2013–2015, with upper ocean temperature anomalies of up to
2 6.2°C relative to 2002-2012 (Gentemann et al., 2017). This event, termed the “Blob”, enhanced surface
3 water stratification, decreasing nutrient supply, primary and community production, leading to widespread
4 changes to open ocean and coastal ecosystems, with geographical shifts of key species across trophic levels,
5 mass strandings of marine mammals, seabird mortalities and closures of commercially important fisheries
6 (Cavole et al., 2016; Piatt et al., 2020). The heatwave reappeared in 2019 (“Blob 2.0”) (Amaya et al., 2020),
7 with similarly high temperature anomalies extending from Alaska to California, but the ecological effects of
8 this event are expected to differ because the Blob originated in winter, and Blob2.0 intensified in summer
9 (Amaya et al., 2020). Modelling suggests rapid shifts in the geographic distributions of important fish
10 species in response to MHWs (Cheung & Frölicher, 2020), with projected decreased biomass and
11 distributional shifts of fish at least four times faster and larger than the effects of decadal-scale mean changes
12 throughout the 21st century under RCP8.5 (high confidence) (Cheung & Frölicher, 2020). Marine heatwaves
13 can also dramatically increase CH4 emissions from oceans, a significant positive feedback on global
14 warming (See also Chapter 3; Borges et al., 2019).
15
16 The Arctic region is warming more than twice as fast as the global mean, and polar organisms and
17 ecosystems are likely to be particularly vulnerable to heatwaves due to their specific thermal niches and
18 physiological thresholds and the lack of poleward ‘refugia’ (high confidence). The consequences of MHWs
19 are exacerbated by concomitant sea-ice melting and freshening of surface waters, leading to secondary
20 effects due to osmotic stress and failing pH homeostasis. Since sea-ice associated organisms are often critical
21 components of polar food chains, cascading effects up to top predators are expected. In 2015–2016 a MHW
22 occurred in the Gulf of Alaska/Bering Sea (Walsh et al., 2018) which was unprecedented in terms of surface
23 temperatures and ocean heat content, geographical extent, depth range and persistence, impacting the entire
24 marine food web. Persistent warming favoured some phytoplankton species and triggered one of the largest
25 algal blooms recorded in this region, with concomitant oyster farm closures due to uncommon paralytic
26 shellfish poisoning events (Walsh et al., 2018). There were also massive die-offs of common murres (Uria
27 aalge) and puffins (Fratercula cirrhata), attributed to starvation resulting from warming-induced effects on
28 food supply (Jones et al., 2019). A 2017 survey found a 71% decline in abundance of Pacific cod (Gadus
29 macrocephalus) since 2015, likely due to an increase in metabolic demand and reduced prey supply during
30 the MHWs (Barbeaux et al., 2020).
31
32 Terrestrial Heatwaves
33
34 Heatwaves are now regularly occurring that exceed the physiological thresholds of some species, including
35 birds and other small endotherms such as flying-foxes (high confidence) (Sections 2.4.2.2, 2.4.2.6).
36 Heatwaves in Australia, North America and southern Africa have caused mass mortality events due to lethal
37 hyperthermia and dehydration (Saunders et al., 2011; Conradie et al., 2020; McKechnie et al., 2021),
38 reducing fitness (du Plessis et al., 2012; Andrew et al., 2017; Sharpe et al., 2019; van de Ven et al., 2019;
39 van de Ven et al., 2020), breeding success and recruitment (Kennedy et al., 2013; Wiley and Ridley, 2016;
40 Ratnayake et al., 2019) and affecting daily activity and geographic distributions (Albright et al., 2017). They
41 also place enormous demands on wildlife management agencies and pose human health risks (Welbergen et
42 al., 2008).
43
44 Recent mortality events affected 14 species of bird and fruit bats (Epomophorus wahlbergi) in South Africa
45 when maximum air temperatures exceeded 43–45°C in 2020 (McKechnie et al., 2021). Passerine birds seem
46 more vulnerable to lethal hyperthermia due to the relative inefficiency of panting to lose heat (McKechnie et
47 al., 2021) and their small size, as heat tolerance generally increases with body mass (McKechnie et al.,
48 2017). Several mass mortality events of flying-foxes (Pteropus poliocephalus, P. alecto) have occurred in
49 eastern Australia when maximum air temperatures exceeded 42℃ (Welbergen et al., 2008). Nineteen such
50 events occurred between 1994 and 2008, compared to three events prior to 1994. In January 2002, maximum
51 temperatures exceeded the 30-year average mean daily maximum by up to 16.5℃ and killed more than 3500
52 individuals (Welbergen et al., 2008). In 2014, an estimated 45,500 flying-foxes died in a single day, when
53 average maximum temperatures were 8°C or more above average (Meteorology, 2014). Drought compounds
54 the impacts, as mortality increases when water availability is low (Welbergen et al., 2008; Mo and Roache,
55 2020; McKechnie et al., 2021).
56
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1 Antarctica encountered its first recorded heatwave in 2020. Record-high temperatures occurred in East
2 Antarctica (Robinson et al., 2020), with a maximum (9.2℃) temperature ~7℃ above the mean maximum,
3 and minimum temperatures > 0℃. Record-high temperatures (18.3℃) were also recorded in West Antarctica
4 (Robinson et al., 2020). It is too soon to know the impacts on polar life, but such abrupt heating is expected
5 to have wide-ranging effects on biota, from flash-flooding and dislodgement of plants, to excess melt waters
6 supplying moisture to arid polar ecosystems (CCP Polar). Heatwaves in Siberia in 2016, 2018 and 2020,
7 with air temperature anomalies >6℃, were associated with extensive wildfires, pest infestations and melting
8 permafrost (Overland and Wang, 2021).
9
10 Freshwater Extremes
11
12 Heatwaves, storms and floods affect the thermal regime and biogeochemical functioning of lakes and rivers
13 (Woolway and Merchant, 2017; Vicente-Serrano et al., 2020). Extreme heatwaves lead to abnormally high
14 water temperatures (Till et al., 2019) and reduce mixing of lakes (Woolway et al., 2021), causing a decrease
15 in oxygen and deep-water oxygen renewal (Zhang et al., 2015). Ectotherms such as fish and invertebrates are
16 particularly susceptible to such temperature and oxygen stress (Stoks et al., 2014). Their metabolic demands
17 increase with rising temperature and suitable habitat is eroded due to both high temperatures and lower
18 oxygen concentrations in lakes and rivers. Till et al. (2019) attributed 502 fish kill events in Wisconsin lakes
19 (USA) to warmer summers in lakes that experienced abnormally high water temperatures. Such events are
20 predicted to double by 2041–2059 and increase fourfold by 2081–2099 compared to historical levels (Till et
21 al., 2019). This anticipated increase in die-offs may facilitate warm-water fish species displacing cool-water
22 species (Hansen et al., 2017; Jennings et al., 2021). Floods mobilise nutrients and sediment, and aid dispersal
23 of invasive species in rivers (Death et al., 2015), while drought extremes reduce river connectivity,
24 threatening biodiversity in rivers ( Section 2.3.3.5; Tickner et al., 2020).
25
26 Learnings from Recent Extremes
27
28 These examples show that the impact of an extreme event is a function of its characteristics and those of the
29 exposed ecosystem. The timing, frequency, absolute magnitude and geographic extent of the extreme event,
30 relative to antecedent conditions, the life-cycle, resistance and resilience of the natural community, all
31 determine the biological response (Figure Cross-Chapter Box EXTREMES.2; Hillebrand et al., 2018; Gruber
32 et al., 2020). Impacts appear to be greater when extreme events occur more frequently, particularly when the
33 interval between events is insufficient to allow recovery to previous population sizes (e.g. frequent fire, coral
34 bleaching), or coincides with vulnerable life cycle stages, even when populations are adapted to cope with
35 such disturbances. Events occurring over large spatial areas reduce the potential for recolonisation from
36 nearby populations (e.g. regional droughts causing widespread declines). Often the magnitude of extreme
37 events exceeds historical levels, so organisms are less likely to be adapted to them, particularly when several
38 extremes coincide (e.g. high water temperatures, drought) (Duke et al., 2017). When hazards occur
39 simultaneously (compound events), impacts of extremes can be substantially aggravated, triggering
40 cascading effects in ecosystems (Gruber et al., 2020).
41
42
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1
2 Figure Cross-Chapter Box EXTRMES.1: A conceptual illustration of how extinction risk is affected by changes in
3 the frequency, duration and magnitude of extreme weather or climate events (e.g,. drought, fire, flood, heatwaves).
4 Many organisms have adapted to cope with long-term and short-term climate variability, but as the magnitude and
5 frequency of extreme events increases, superimposed on the long-term climate trend, the threshold between survivable
6 extreme weather events (yellow) and extremes that have high risk of causing population or species extinctions (red) is
7 crossed more frequently. This can lead to local extinction events with insufficient time between to enable recovery,
8 resulting in long-term, irreversible changes to the composition, structure and function of natural systems. When the
9 extreme event occurs over a large area relative to the distribution of a species (e.g., a hurricane impacting an island
10 which is the only place a given species occurs), a single extreme event can drive the global extinction of a species.
11
12
13 Several characteristics of natural systems are associated with greater vulnerability to extreme events (Figure
14 Cross-Chapter Box EXTREMES.2), knowledge of which can inform solutions to build resilience and aid
15 recovery (Robinson et al., 2020). Resilience can be built prior to an event by minimising additional
16 disturbances, such as water extraction from river systems, pollution of aquatic systems, land-use change and
17 fragmentation. Managing landscapes to reduce fragmentation and increase habitat extent, connectivity and
18 heterogeneity, by increasing the number and extent of reserves, may provide local refugia from extreme
19 events and enhance post-event recolonisation, but may be less effective for marine systems (Chapter 3,
20 Section 3.6). Maintaining taxonomic, phylogenetic and functional diversity is important, as more diverse
21 systems may be more stable in the face of disturbance (Pimm, 1984; García-Palacios et al., 2018).
22
23 Several characteristics increase vulnerability: low or narrow thermal tolerances, high habitat specificity, low
24 dispersal ability, long generation times, low competitive ability and lifecycle constraints that limit recovery
25 or recolonisation. Populations living close to one or more limiting factors near range edges are also
26 vulnerable (Arafeh-Dalmau et al., 2019). Understanding these characteristics can inform management
27 intervention to aid recovery following an extreme event. For instance, knowledge of the flying-fox’s
28 physiological temperature threshold led to successful interventions, including misting of populations to
29 reduce mortality (Mo and Roache, 2020) and the development of a ‘heat stress forecaster’, an online tool
30 which uses weather forecasts to identify roosts at risk of extreme heat events (Ratnayake et al., 2019). This
31 early-warning system increases the preparedness of wildlife management and conservation agencies,
32 enabling efficient allocation of management resources towards locations that are likely to be most affected.
33 Monitoring following extreme events can help identify immediate impacts and the potential for cascading
34 interactions, such as changes to competitive interactions following range shifts, impacts on freshwater
35 ecosystems following wildfires and the spread of invasive species. Ongoing monitoring of recovery and
36 effectiveness of management intervention is important, focussing on habitat-forming species (eg. kelp,
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1 corals, dominant tree species) and keystone species (eg. filter feeders, macrophytes, top predators), as the
2 loss of these species can lead to ecosystem tipping points, beyond which the system may not recover
3 (Section 2.5.3; SROCC Chapter 6, Section 3.4.4.1; AR6 sectoral chapters, Chapter 3 section 3.4.4.1.4).
4 The acute impacts of extreme events, in addition to the chronic stress of changing mean conditions, are
5 accelerating and amplifying the biological effects of climate change. This amplification is being observed
6 globally and in all realms where life exists. Extreme events are compressing the timeline available for natural
7 systems to adapt and impeding our ability to identify, develop and adopt solutions. Recent events highlight
8 the urgent need to mitigate global greenhouse gas emissions and identify solutions to halt accelerating
9 impacts on natural systems (Díaz et al., 2020).
10
11
12
13 Figure Cross-Chapter Box EXTREMES.2: Characteristics of natural systems that affect vulnerability and help
14 identify solutions – both prior to and after extreme events - to build resistance, resilience and recovery.
15
16
17 [END CROSS-CHAPTER BOX EXTREMES HERE]
18
19
20 2.4 Observed Impacts of Climate Change on Species, Communities, Biomes, Key Ecosystems and
21 their Services
22
23 2.4.1 Overview
24
25 Global meta-analyses of terrestrial systems in AR3 and AR4 concentrated on long time frames (>20 years)
26 and findings from relatively undisturbed areas, where confidence in attributing observed changes to climate
27 change is high. Recent global and regional meta-analyses (AR5 and later) have been broader, including data
28 from degraded and disturbed areas and studies with shorter time frames (Tables 2.2a,b).
29
30 By the time of AR5, >4000 species with long-term observational data had been studied in the context of
31 climate change (Parmesan, 2006; Parmesan and Hanley, 2015). Since then, hundreds of new studies have
32 been added, leading to higher confidence in climate change attribution (Table 2.2; Scheffers et al., 2016;
33 Wiens, 2016; Cohen et al., 2018; Feeley et al., 2020). Freshwater habitats have been under-represented in
34 prior reports, but new long-term data sets, coupled with laboratory and field experiments, are improving our
35 understanding and this assessment stresses observations from lakes and streams. As numbers of studies
36 increase and data is increasingly extracted from areas with high LULCC, attribution is more difficult as
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1 habitat loss and fragmentation are known major drivers of changes in terrestrial and freshwater species
2 (IPBES) (Gardner and Finlayson, 2018; Grill et al., 2019; Zarfl et al., 2019; Tickner et al., 2020).
3
4 2.4.2 Observed Responses to Climate Change by Species and Communities (Freshwater and Terrestrial)
5
6 2.4.2.1 Observed Range Shifts Driven by Climate Change
7
8 Poleward and upward range shifts were already attributable to climate warming with high confidence in
9 AR5. Publication of observed range shifts in accord with climate change have accelerated since AR5 and
10 strengthened attribution. Ongoing latitudinal and elevational range shifts driven by regional climate trends
11 are now well-established globally across many groups of organisms, and attributable to climate change with
12 very high confidence due to very high consistency across a now very large body of species and studies and
13 in-depth understanding of mechanisms underlying physiological and ecological responses to climate drivers
14 (Table 2.2; Table 2.3, Table SM2.1;Pöyry et al., 2009; Chen et al., 2011; Grewe et al., 2013; Gibson-
15 Reinemer and Rahel, 2015; MacLean and Beissinger, 2017; Pacifici et al., 2017; Anderegg et al., 2019b).
16 Range shifts stem from local extinctions along warm-range-boundaries (Anderegg et al., 2019b), as well as
17 from colonisation of new regions at cold-range-boundaries (Ralston et al., 2017).
18
19 Many studies since AR4 have tended not to be designed as attribution studies, particularly recent large scale,
20 multispecies meta-analyses. That is, all data available were included in such studies (from both undisturbed
21 and from highly degraded lands, and including very short term datasets of <20 years) with little attempt to
22 design the studies to differentiate effects of climate change from effects of other potential confounding
23 variables. These studies tended to find greater lag and lower proportion of species changing in directions
24 expected from climate change, with authors concluding that LULCC, particularly habitat loss and
25 fragmentation, was impeding wild species from effectively tracking climate change (Lenoir and Svenning,
26 2015; Rumpf et al., 2019; Lenoir et al., 2020).
27
28 Unprecedented outbreaks of spruce beetles occurring from Alaska to Utah in the 1990s were attributed to
29 warm weather that, in Alaska, facilitated a halving of the insect’s life cycle from two years to one (Logan et
30 al., 2003). Milder winters and warmer growing seasons were likewise implicated in poleward range
31 expansions and increasing outbreaks of several forest pests (Weed et al., 2013), leading to the current
32 prediction that 41% of major insect pest species will increase their damage further as climate warms, and
33 only 4% will reduce their impacts, while the rest will show mixed responses (Lehmann et al., 2020).
34
35 During their range shifts, forest pests remain climate-sensitive. For example, the distribution of Western
36 Spruce Budworm is limited at its warm range edges by adverse effects of mild winters on overwinter
37 survival, and at its cool range limits by ability to arrive at a cold-resistant stage before winter arrives
38 (Régnière and Nealis, 2019). We might therefore expect tree mortality from insect outbreaks to be most
39 severe in sites climatically less suitable for the plants, where plants would be under more stress. However,
40 (Jaime et al., 2019), using separate SDMs (MaxEnt) for the insects and plants, found that mortality of Scots
41 Pine from bark beetles was highest in sites most climatically suitable for the trees as well as for the insects.
42 In a study of tree mortality in California, bark beetles selectively killed highly-stressed fir trees but killed
43 pines according to their size, irrespective of stress status (Stephenson et al., 2019).
44
45 Range shifts in a poleward and upward direction, following expected trajectories given the local and regional
46 climate trends, are strongly occurring in freshwater fish populations in North America (Lynch et al., 2016b),
47 Europe (Comte and Grenouillet, 2013; Gozlan et al., 2019) and Central Asia (Gozlan et al., 2019). Cold
48 water fish, such as coregonids and smelt have been negatively affected at the equatorial borders of their
49 distributions (Jeppesen et al., 2012). Upward elevational range shifts in rivers and streams have been
50 observed. Systematic shifts towards higher elevation and upstream were found for 32 stream fish species in
51 France following regional variation in climate change (Comte and Grenouillet, 2013). Bull trout (Salvelinus
52 confluentus) in Idaho (USA), were estimated to have lost 11–20% (8–16% decade-1) of the headwater stream
53 lengths necessary for cold water spawning and early juvenile rearing, with the largest losses occurring in the
54 coldest habitats (Isaak et al., 2010). Range contractions of the same species have been found in the Rocky
55 Mountain watershed (Eby et al., 2014). Likewise, the distribution of the stonefly Zapada glacier, endemic to
56 alpine streams of Glacier National Park in Montana (USA), has been reduced over several decades by
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1 upstream retreat to higher, cooler sites as water temperatures have increased and glacial masses decreased
2 (Giersch et al., 2015).
3
4 The melting of glaciers has led to a change in water discharge associated with community turnover in
5 glacier-fed streams (Cauvy-Fraunié and Dangles, 2019). For instance, glacier‐obligate macroinvertebrates
6 have started disappearing when glacial cover drops below approximately 50% (robust agreement, high
7 confidence), reviewed in (Hotaling et al., 2017). For freshwater invertebrates, no meaningful trends have
8 been detected in geographic extent or population size for most species (Gozlan et al., 2019).
9
10 An invasive freshwater cyanobacterium in lakes, Cylindrospermopsis raciborskii, originating from the
11 tropics, has spread to temperate zones over the last few decades due to climate change-induced earlier
12 increase of water temperature in spring (Wiedner et al., 2007), aided by a competitive advantage in eutrophic
13 systems (Ekvall et al., 2013; Urrutia-Cordero et al., 2016).
14
15 2.4.2.2 Observed Local Population and Global Species' Extinctions Driven by Climate Change
16
17 Disappearances of local populations within a species range are more frequent and better documented than
18 whole species' extinctions, and attribution to climate change is possible for sites with minimal confounding
19 non-climatic stressors. Changes of temperature extremes are often more important to these local extinction
20 rates than changes of mean annual temperature (see Sections 2.3.1, 2.3.2, 2.3.3.5, 2.4.2.6, Cross-chapter Box
21 EXTREMES this Chapter; Parmesan et al., 2013). In a study of 538 plant and animal species, sites with local
22 extinctions were associated with smaller changes of mean annual temperature but larger and faster changes
23 of hottest yearly temperatures than sites where populations persisted (Román-Palacios and Wiens, 2020).
24 Near warm range limits, 44% of species had suffered local extinctions. In both temperate and tropical
25 regions, sites with local extinction had greater increases in maximum temperatures than those without (Tmax
26 increased 0.456°C and 0.316°C vs. Tmean increase of 0.153 °C and 0.061 °C for temperate (n=505 sites) and
27 tropical (n=76 sites), respectively, P < 0.001) (Román-Palacios and Wiens, 2020).
28
29 Wiens (2016) assumed that population extinctions were primarily driven by climate change when they
30 occurred at elevational or latitudinal "warm edge" range limits, and were in relatively undisturbed sites that
31 were stated by authors to be under increasing climatic stress. By this criterion, climate-caused local
32 extinctions were widespread among plants and animals, detected in 47% of 976 species examined. The
33 percentage of species suffering these extinctions was higher in the tropics (55%), than in temperate habitats
34 (39%), higher in freshwater (74%), than in marine (51%) or terrestrial (46%) habitats and higher in animals
35 (50%) than in plants (39%). The difference between plants and animals varied with latitude: in the temperate
36 zone a much higher proportion of animals than plants suffered range-limit extinctions (38.6% of 207 animal
37 species versus 8.6% of 105 plants, p < 0.0001) while at tropical sites local extinction rates were
38 (nonsignificantly) higher in plants (59% of 155 species) than in animals (52% of 349 species), the reverse of
39 their temperate zone relationship. Rates varied among animal groups, from 35% in mammals through 43% in
40 birds to 56% in insects and 59% in fish (Wiens, 2016).
41
42 Freshwater population extinctions are mainly due to habitat loss, introduction of alien species, pollution,
43 over-harvesting (Gozlan et al., 2019; IPBES, 2019) and climate change induced epidemic diseases (Pounds
44 et al., 2006)(see Section 2.4.2.7.1). Climate warming particularly through intensification and severity of
45 droughts, contributes to the disappearance of small ponds, which hold rare and endemic species (Bagella et
46 al., 2016). Systematic data on the extent and biology of small ponds is, however, lacking at a global scale.
47 Extreme heat waves can lead to large local fish kills in lakes (see Section 2.3.3.5), when water temperature
48 and oxygen concentrations surpass critical thresholds, threatening cold water fish and amphibians
49 (Thompson et al., 2012). Evidence for a local extinction of some invertebrate species with a 1.4°–1.7°C rise
50 in mean annual stream winter temperature from 1981-2005 was reported in (Abrahams et al., 2013).
51 Population declines of specialist species in glacier-fed streams, such as the non-biting midge Diamesa davisi
52 (Chironomidae), can be attributed to glacier retreat given climate change (Cauvy-Fraunié and Dangles,
53 2019), and the flatworm Crenobia alpina (Planariidae) has been reported as locally extinct in the Welsh Llyn
54 Brianne river (Durance and Ormerod, 2010; Larsen et al., 2018).
55
56 Many high montane possums in Australia have low physiological tolerance to heatwaves, with death
57 occuring due to heat-driven dehydration at temperatures exceeding 29°–30°C for >4–5 hours over several
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1 days (Meade et al., 2018; Turner, 2020), with major declines recorded for several species, and population
2 extinctions at lower elevations, since the early 2000s (Chandler, 2014; Weber et al., 2021).
3
4 Two terrestrial and freshwater species have gone extinct, with climate change implicated as a key driver. The
5 cloud-forest-restricted Golden toad (Incilius periglenes) was extinct by 1990 in a nature preserve in Costa
6 Rica, driven by successive extreme droughts. This occurred in the absence of chytridiomycosis infection,
7 caused by the fungal pathogen Batrachochytrium dendrobatidis (BD), verified during field censuses of
8 golden toad populations in the process of extinction and through genetic analyses of museum specimens,
9 although Bd was present in other frog species in the region (medium evidence, high agreement) (Pounds et
10 al., 1999; Pounds et al., 2006; Puschendorf et al., 2006; Richards-Hrdlicka, 2013). The interaction between
11 expansion of chytrid fungus globally and local climate change is implicated in the extinction of a wide range
12 of tropical amphibians (see Section 2.4.2.7.1 Case study 2 Chytrid fungus and climate change).
13
14 The Bramble Cay Melomys (Melomys rubicola), the only mammal endemic to the Great Barrier Reef,
15 inhabited a small (five hectare) low-lying (<3m high) cay in the Torres Strait Islands, Australia. Recorded
16 having a population size of several hundred in 1978, this mammal has not been seen since 2009 and was
17 declared extinct in 2016 (Gynther et al., 2016). SLR, documented increases in storm surge and in tropical
18 cyclones, driven by climate change, led to multiple inundations of the island in the 2000s. Between 1998 and
19 2014, herbacious vegetation, the food resource for the BC Melomys, declined by 97% in area (from 2.2 ha
20 down to 0.065 ha), and from 11 plant species down to two (Gynther et al., 2016; Watson, 2016; Woinarski,
21 2016; Woinarski et al., 2017). The island was unihabited with few non-climatic threats, providing high
22 confidence in attribution of extinction of the BC Melomys to climate change-driven increases in frequency
23 and duration of island inundation (Turner and Batianoff, 2007; Woinarski et al., 2014; Gynther et al., 2016;
24 Watson, 2016; Woinarski et al., 2017).
25
26 In the IUCN Red List (IUCN, 2019), 16.2% of terrestrial and freshwater species (n=3,777 species) that are
27 listed as endangered, critically endangered or extinct in the wild (n=23,251 species) list climate change or
28 severe weather as one of their threats.
29
30
31 [START FAQ2.1 HERE]
32
33 FAQ2.1: Will species go extinct with climate change and is there anything we can do to prevent it?
34
35 Climate change is already posing major threats to biodiversity and the most vulnerable plants and animals
36 are likely to go extinct. If climate change continues to worsen, it is expected to cause many more species to
37 go extinct unless we take actions to improve the resilience of natural areas, through protection, connection
38 and restoration. We can also help individual species that we care most about by reducing the stress they are
39 under from other human activities, and even helping them move to new places as their climate space shifts
40 and they need to shift to keep up.
41
42 Climate change has already caused some species to go extinct, and is likely to drive more species to
43 extinction. Species have always gone extinct in the history of our planet but human activities causing climate
44 change are accelerating this process. For instance, recent research predicts that one-third of all plant and
45 animal species could be extinct by 2070 if climate change continues as it is. Species can adapt to some extent
46 to these rapidly changing climate patterns. We are seeing changes in behaviour, dispersal to new areas as the
47 climate becomes more suitable, and genetic evolution. However, these changes are small, and adaptations
48 are limited. Species that cannot adapt beyond their basic climate tolerances (ability to survive extremes of
49 temperature or rainfall) or successfully reproduce in a different climate environment from what they have
50 evolved in, will simply disappear. In the Arctic for example, sea ice is melting and will likely disappear in
51 summer time within a century. This means that the animals that have evolved to live on sea ice - polar bears
52 and some seals and sea lions - will go extinct.
53
54 Fortunately, there are some things we can do to help. We can take actions to assist, protect and conserve
55 natural ecosystems and prevent the loss of our planet’s endangered wildlife, such as:
56
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1 “Assisting” species’ migration: This has many names, “assisted colonisation”, "assisted translocation",
2 "assisted migration", "assisted movement". In effect, it is about helping endangered species to move to a new
3 area with a good habitat for them to survive. "Passive" assisted colonisation focuses on helping species move
4 themselves, whilst the most “active” form implies picking up individuals and transporting them to a new
5 location. This is different from re-introductions that are already a normal part of conservation programs.
6 Climate-driven translocations are moving plants or animals to an area where they have never lived
7 historically, a new location that is now suitable for them due to climate change.
8
9 This active form of “assisted colonisation” has been controversial, because exotic species can become
10 invasive when they are moved between continents or oceans. For example, no one would advocate moving
11 polar bears to Antarctica, as they would likely fest on native penguins, thus causing another conservation
12 problem. However, moving species only a few hundred kilometers avoids most adverse outcomes, and that
13 is often all that is needed to help a wild plant or animal cope with climate change. In extreme cases, another
14 type of assisted adaptation is to preserve species until we get climate change under control and can
15 reintroduce them to the wild. This might include moving them into zoos or into seed or frozen embryo
16 banks.
17
18 Extending protected zones and their connectivity: Species' ability to move to new locations and track climate
19 change are very limited – in particular, when a habitat has been turned into a crop field or a city. To help
20 them move between their natural habitats, we can increase the connectedness of protected areas or simply
21 create small patches or corridors of semi-wild nature within a largely agricultural or inhabited region, that
22 encourages wildlife to move through an area, and in which they are protected from hunting and poisons.
23 Those semi-wild protected areas can be very small, like the hedgerows between fields in England, that
24 provide both habitats for many flowers, birds, insects and corridors to move between larger protected areas.
25 Alternatively, it can just be an abandoned field that is now growing "weeds" without pesticides, hunting or
26 farming. For instance, in the United States of America, private landowners get a tax break by making their
27 land a "wildlife conservation" area using no pesticide, not cutting weeds too often, putting up brush piles and
28 bird boxes for nesting, and providing a water source.
29
30 Assisting, protecting and conserving natural ecosystems would help enhance biodiversity overall as well as
31 already endangered species. Diverse plant and animal communities are more resilient to disturbances,
32 including climate change. A healthy ecosystem also recovers more quickly from extreme events, such as
33 floods, droughts and heat waves that are a part of human-driven climate change. Healthy ecosystems are
34 critical to prevent species’ extinctions from climate change, but are also important for human health and
35 well-being, providing clean, plentiful water, cleaning the air, providing recreation and holiday adventures,
36 and making people feel happier, calmer and more contented.
37
38
39
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1 Figure FAQ2.1.1: Possible actions to assist protect and conserve natural ecosystems and prevent the loss of our
2 planet’s endangered wildlife in the face of continued climate change. (Inspired by Natural Alliance website © Chris
3 Heward/GWCT)
4
5 [END FAQ2.1 HERE]
6
7
8 2.4.2.3 Observed Changes in Community Composition Driven by Climate Change
9
10 2.4.2.3.1 Overall patterns of community change
11 The most common type of community change takes the form of in situ decreases of cold-adapted species and
12 increases of warm-adapted species (Bowler et al., 2017; Hughes et al., 2018; Kuhn and Gégout, 2019; Feeley
13 et al., 2020). This process has lead to increases of species richness on mountaintops and decreased richness
14 at adjacent lower elevations (medium evidence, high agreement) (Forister et al., 2010; Steinbauer et al.,
15 2018). Observed shifts in community composition have consequences for species' interactions. Such indirect
16 effects of climate change have been shown to often have greater impacts on species than direct effects of
17 climate itself, particularly for higher level consumers (Ockendon et al., 2014).
18
19 Like other responses, analyses indicated responses were lagging behind the change expected from regional
20 warming, and thereby accumulating 'climate debt.' Examples of climate debt, measured from community
21 composition changes, come from birds and butterflies in Europe (Devictor et al., 2012) and from lowland
22 forest herbaceous plants in France (Bertrand et al., 2011). The French study found that larger debts occurred
23 in communities with warmer baseline conditions and that some of the apparent debt stemmed from species'
24 ability to tolerate warming in situ. Geothermal streams have provided evidence about community structure
25 and ecosystem function in high temperatures. A study of 14 such habitats reported simplified the food-web
26 structures and shortened pathways of energy flux between consumers and resources (high confidence)
27 (O’Gorman et al., 2019).
28
29 Prominent changes in freshwater community composition, such as increases in cyanobacteria and warm
30 tolerant zooplankton species, loss of cold water fish, gain in thermo-tolerant fish and macroinvertebrates and
31 gain in floating macrophytes, are occurring (medium evidence, high agreement, medium confidence) (Adrian
32 et al., 2016; Hossain et al., 2016; Short et al., 2016; Huisman et al., 2018; Gozlan et al., 2019). Changes in
33 relative species abundances, species composition and biodiversity due to warming trends and non-climate
34 driven changes are to be expected in lakes and rivers globally. However, thus far empirical evidence and
35 mechanistic understanding to inform modelling is too limited to draw general conclusions about the nature of
36 current and future climate change driven changes within entire food webs on a global scale (Urban et al.,
37 2016).
38
39 2.4.2.3.2 Freshwater mechanistic drivers and responses
40 Physical changes in lakes (see Section 2.3.3) have affected primary production (see Section 2.4.4.5.2), algal
41 bloom formation and composition, zooplankton and fish size distribution and species composition (Urrutia-
42 Cordero et al., 2017; Gozlan et al., 2019; Seltmann et al., 2019). Declines in abundance of cold-stenothermal
43 species (particularly Arctic charr, Salvelinus alpinus, coregonids and smelt) and increases in eurythermal
44 fish (e.g. the thermo-tolerant carp Cyprinus carpio, common bream, pike perch, roach and shad) have been
45 observed in northern temperate lakes associated with warming trends (high agreement, medium confidence)
46 (Jeppesen et al., 2012; Jeppesen et al., 2014). These changes increase predation pressure on zooplankton and
47 reduce grazing pressure on phytoplankton, which may result in higher phytoplankton biomass (De Senerpont
48 Domis et al., 2013; Jeppesen et al., 2014; Adrian et al., 2016). Reduction in lake mixing lowers the
49 concentration of nutrients in the epilimnion and may lead to higher silicon to phosphorous ratios negatively
50 affecting diatom growth (Yankova et al., 2017) or overall primary productivity (see Section 2.4.4.5.2).
51 In as study of 1,567 lakes across Europe and North America, (Kakouei, 2021) identified climate change as
52 the major driver of increases in phytoplankton biomass in remote areas with minimal LULCC. Greater
53 temperature variability can be more important than long-term temperature trends as a driver of zooplankton
54 biodiversity (Shurin et al. (2010). Reductions of winter severity attributed to anthropogenic climate change
55 are increasing winter algal biomass, and motile and phototropic species at the expense of mixotrophic
56 species (Özkundakci et al., 2016; Hampton et al., 2017).
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1
2 Tropical lakes are prone to loss of deep-water oxygen due to lake warming with negative consequences for
3 their fisheries and their biodiversity (Lewis Jr, 2000; Van Bocxlaer et al., 2012). Many ancient tropical lakes
4 (Malawi, Tanganyika, Victoria, Titicaca, Towuti and Matano) hold thousands of endemic animal species
5 (Vadeboncoeur et al., 2011).
6
7 Observed climate-change effects on freshwater invertebrates are variable (Knouft and Ficklin, 2017). In
8 glacier-fed streams globally, climate change has caused community turnover and changes in abundances in
9 terms of increased generalist and decreased specialist species abundances (Lencioni, 2018; Cauvy-Fraunié
10 and Dangles, 2019). In turn, dragonflies in flowing waters, monitored during the warming period from 1988
11 through 2006 in Europe, did not show consistent changes in their distribution (Grewe et al., 2013), reviewed
12 in (Knouft and Ficklin, 2017). Long-term trends in species composition and community structure of stream
13 macroinvertebrates, specifically a general trend for decreases in species characteristic of cold, fast-flowing
14 waters and increases of thermophilic species typical of stagnant or slow-moving waters, have been attributed
15 to climate change (high agreement, high confidence) (Daufresne et al., 2007; Chessman, 2015). A study of
16 14 geothermal streams reported simplified food-web structures and shortened pathways of energy flux
17 between consumers and resources (O’Gorman et al., 2019). Macrophytes benefit from rising water
18 temperatures, but increased shading from increased phytoplankton biomass could offset this (see 2.5.3.6.2
19 for projections; Hossain et al., 2016; Short et al., 2016;Zhang et al., 2017a).
20
21
22 2.4.2.3.3 Emergence of novel communities and invasive species
23 As climate change is increasing the movements of species into new areas, there is concern about how exotic
24 species are being impacted, either by becoming invasive or by already invasive species gaining even more
25 advantage over native species. Modeling predicts that effects of climate warming on food web structure and
26 stability favour success of invading species (Sentis et al., 2021). Both simulated warming experiments
27 (Zettlemoyer et al., 2019) and long-term observations (Losos et al., 2010) have found phenologies of exotic
28 species to respond more adaptively to warming than those of natives, and in the long-term observations the
29 success of exotics was attributed to their greater phenological responsiveness. In an expert assessment of the
30 future relative importance of different drivers of the impacts of biological invasions, climate change was
31 named as the most important driver in polar regions, the second most important in temperate regions (after
32 trade/transport) and the third most important in the tropics (after trade/transport and human
33 demography/migration) (Essl et al., 2020).
34 However, not all exotic species become invasive. As novel climate conditions develop, novel communities
35 made up of new combinations of species are emerging as populations and species adapt and shift ranges
36 differentially, not always with negative consequences (high confidence) (Dornelas et al., 2014; Evers et al.,
37 2018; Teixeira and Fernandes, 2020). Novel communities differ in composition, structure, function and
38 evolutionary trajectories, as the proportion of specialists and generalists, native, introduced and range
39 shifting species changes and species interactions are altered, ultimately affecting ecosystem dynamics and
40 functioning (Lurgi et al., 2012; Hobbs et al., 2014; Heger and van Andel, 2019 Towards an Integrative). The
41 exact nature of novel communities is difficult to predict because species-level uncertainties propagate at the
42 community level due to ecological interactions (Williams and Jackson, 2007), but observations, experimental
43 mesocosms (Bastazini et al., 2021); and theoretical models (Lurgi et al., 2012; Sentis et al., 2021) provide
44 support that they will continue to emerge with climate change.
45
46 2.4.2.4 Observed Phenological Responses to Climate Change
47
48 With advances in remote sensing, quality and quantity of phenological data are rapidly increasing (Piao et
49 al., 2019). Since AR5, numbers of studies have increased substantially with consistent conclusions in
50 response to warming, including advancement of spring events and lengthening of growing seasons in
51 temperate regions (through a combination of advancement of spring events and to a lesser extent, retardation
52 of autumn events) (robust evidence, high agreement) (Table 2.2, Table 2.3, Table SM2.1; Menzel et al.,
53 2020). In the tropics, by contrast, precipitation changes have more strongly influenced phenology than
54 temperature changes (Cohen et al., 2018). A meta-analysis comparing observed phenological advances in
55 birds with expectations from warming of local climates concluded that the advances fell short of expectation
56 and that substantial phenological climate debt had been generated (Radchuk et al., 2019).
57
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1 Taxonomic groups have differed in their responses (Parmesan, 2007; Thackeray et al., 2010), and a few have
2 completely abstained from the general trends—for example, seabirds continue to breed with their pre-
3 climate-change phenologies (Keogan et al., 2018). Newer reviews and analyses reveal differences in
4 responses among continents and across time intervals (Piao et al., 2019). Mean advance in days per decade
5 was 5.5 in China, and 3.0-4.2 in Europe but only 0.9 in North America (Piao et al., 2019). Mean values for
6 retardation of autumn leaf fall, which can be more influenced by photoperiod and less by temperature than
7 spring leaf-out, were 0.36 days per decade in Europe (Menzel et al., 2020), 2.6 days per decade in China and
8 around 3 days per decade in the USA (medium evidence, high agreement) (Piao et al., 2019).
9
10 The rapid rates of advance of spring events in the 1990s slowed down in the 2000s and stalled or even
11 reversed in some regions (Menzel et al., 2020). Wang et al. (2019) noted, from remote sensing, that during
12 the 'global warming hiatus' from 1998-2012, there were no global trends in either Spring green-up or
13 autumn colouring. Annual crops, for which timing is determined by farmers, were an exception. When
14 natural systems were advancing fast prior to 1998, farmers advanced more slowly, but during the natural
15 'hiatus', farmed crops advanced faster than wild plants and cultivated trees (Menzel et al., 2020). In a long
16 (67 year) European time series (Menzel et al., 2020), autumn leaf colouring showed delays attributed to
17 winter & spring warming in 57% of observations (mean delay 0.36 days per decade); spring & summer
18 phenologies advanced in 89% of wild plants despite decreased winter chilling, with c.60% of trends
19 significant and 'strongly attributable' to winter & spring warming; and growing season length increased in
20 84% of cases (mean lengthening 0.26 days yr-1) (Table 2.2).
21
22 Changes in freshwater systems are consistent with changes in terrestrial systems: earlier timing of spring
23 phytoplankton and zooplankton development and earlier spawning by fish, as well as extension of the
24 growing season are occurring (robust evidence, high agreement) (Adrian et al., 2009; De Senerpont Domis et
25 al., 2013; Adrian et al., 2016; Thackeray et al., 2016). Phenological changes in lakes have been related to
26 rising water temperatures, reductions of ice cover and prolongation of thermal stratification (increasing
27 evidence and agreement since AR5; very high confidence). Crozier and Hutchings (2014) reviewed
28 phenological changes in fish and documented that changes in the timing of migration and reproduction, age
29 at maturity, age at juvenile migration, growth, survival and fecundity were associated primarily with changes
30 in temperature. The median return time of Atlantic salmon among rivers in Newfoundland and Labrador
31 advanced by 12 to 21 days over the past decades, associated with overall warmer conditions (Dempson et al.,
32 2017).
33
34 2.4.2.5 Observed Complex Phenological and Range Shift Responses
35
36 Early meta-analyses tested the straightforward hypotheses that warming should shift timing earlier and
37 ranges poleward. Once these trends had been established, exceptions to them became foci of study. For
38 example, some plants in northern regions of the northern hemisphere were retarding their spring flowering
39 instead of advancing it as expected with warming. These turned out to be species requiring vernalisation
40 (winter chilling) to speed spring development. For these plants, phenological changes result from combined
41 effects of advancement caused by spring warming and retardation caused by winter warming. Incorporating
42 this level of complexity into analyses revealed that a greater proportion of species were responding to
43 climate change than estimated under the simple expectation that warming should always cause advancement
44 (92% responding vs 72% from earlier analyses) (Cook et al., 2012).
45
46 Animal species can show vernalisation equivalent to that in plants (Stålhandske et al., 2017). However, a
47 semi-global meta-analysis across terrestrial animals failed to detect delaying effects of warming winters
48 (Cohen et al., 2018). The same animal-based meta-analysis contrasted phenological changes in temperate-
49 zone animals, which are principally explained by changes of temperature, with those at lower latitudes,
50 which follow changes of precipitation (Cohen et al., 2018).
51
52 Vitasse et al. (2018), working with Alpine trees, found that phenological delay with increasing elevation had
53 declined from 34 days per 1,000 m in the 1960s to 22 days per 1,000 m, greatly reducing the differences in
54 timing between trees growing at different elevations. This reduction was greatest after warmer winters,
55 suggesting winter warming as a principal cause of the overall trend.
56
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1 Lian et al. (2020) observed that earlier spring leaf-out in the Northern Hemisphere is causing increases in
2 evapotranspiration that are not fully compensated by increased precipitation. The consequence is increased
3 soil moisture deficit in summer, expected to exacerbate impacts of heatwaves as well as drought stress. In
4 Arctic freshwater ecosystems, Heim et al. (2015) demonstrated the importance of seasonal cues for fish
5 migration, which can be impacted by climate change due to reduced stream connectivity and fragmentation,
6 earlier peak flows, and increased evapotranspiration.
7
8 Precipitation has also been implicated in exceptions to the rule that ranges should be shifting to higher
9 elevations. In dry climates, increases of precipitation accompanying climate warming can facilitate
10 downslope range shifts (Tingley et al., 2012).
11
12 Multiple responses can co-occur. Hällfors et al (Hällfors et al., 2021), in a study of 289 lepidoptera in
13 Finland, found 45% had either shifted their ranges northward or advanced their flight season with warming.
14 The 15% of species that did both (shifting northward by 113.1 km and advancing flight period by 2.7 days
15 per decade, on average, over a 20 year period) had the largest population increases, and the 40% of species
16 that showed no response had the largest population declines.
17
18
19 Table 2.2: Global Fingerprints of Climate Change Impacts across Wild Species. Updated from (Parmesan and Hanley,
20 2015). For each dataset, a response for an individual species or functional group was classified as (1) no response (no
21 significant change in the measured trait over time), (2) if a significant change was found, the response was classified as
22 either consistent or not consistent with expectations from local or regional climate trends. Percentages are approximate
23 and estimated for the studies as a whole. Individual analyses within the studies may differ. The specific metrics of
24 climate change analysed for associations with biological change vary somewhat across studies, but most use changes in
25 local or regional temperatures (e.g. mean monthly T or mean annual T), with some using precipitation metrics (e.g. total
26 annual rainfall). For example, a consistent response would be poleward range shifts in areas that are warming.
27 Probability (P) of getting the observed ratio of consistent: not consistent responses by chance was <10-13 for (Parmesan
28 and Yohe, 2003; Root et al., 2003; Root et al., 2005; Poloczanska et al., 2013) and was <0.001 for Rosenzweig 2008
29 (source=publication) (Parmesan and Yohe, 2003; Root et al., 2003; Root et al., 2005; Rosenzweig et al., 2008;
30 Poloczanska et al., 2013). Test were all binomial tests against p=0.5, performed by Parmesan.
31 [INSERT TABLE 2.2 HERE.]
32
33
34 2.4.2.6 Observed Changes to Physiology and Morphology Driven by Climate Change
35
36 Impacts on species physiology in terrestrial and freshwater systems have been observed and attributed to
37 climate change (medium confidence), including changes in tolerances to high temperatures (Healy and
38 Schulte, 2012; Gunderson and Stillman, 2015; Deery et al., 2021), increased metabolic costs of living under
39 elevated temperatures (Scheffers et al., 2016) and shifts in sex ratios in species with temperature-dependent
40 sex determination (e.g. masculinisation of lizard populations (Schwanz and Janzen, 2008; Schwanz, 2016;
41 Edmands, 2021) and feminisation of turtle populations (Telemeco et al., 2009)). Skewed sex ratios can lead
42 to mate shortages, reduced population growth and adaptive potential, and increased extinction risk, because
43 genetic diversity decreases as fewer individuals mate and heterozygosity is lost (Mitchell and Janzen, 2010;
44 Edmands, 2021).
45
46 Behavioural plasticity such as nest-site selection can provide a partial buffer from the effects of increasing
47 temperature, but there are environmental and physical limits to this plasticity (medium confidence)
48 (Refsnider and Janzen, 2016; Telemeco et al., 2017). Plasticity in heat tolerance (e.g. due to reversible
49 acclimation or acclimatisation) can also potentially compensate for rising temperatures (Angilletta Jr, 2009),
50 but ectotherms have relatively low acclimation in thermal tolerance and acclimation is expected to only
51 slightly reduce overheating risk in even the most plastic taxa (low confidence) (Gunderson and Stillman,
52 2015).
53
54 Geographic variation in thermal tolerance plasticity is expected to influence species vulnerability and range
55 shifts in response to climate change (Gunderson and Stillman, 2015; Sun et al., 2021). In many ectotherms,
56 plasticity in thermal tolerance increases towards the poles, as thermal seasonality increases (Chown et al.,
57 2004), contributing to higher vulnerability to warming in tropical organisms (low confidence) (Huey et al.,
58 2009; Campos et al., 2021). Some species have evolved extreme upper thermal limits at the expense of
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1 plasticity, reflecting an evolutionary trade-off between these traits (Angilletta et al., 2003; Stillman, 2003).
2 The most heat-tolerant species, such as those from extreme environments, may therefore be at greater risk of
3 warming because of an inability to physiologically adjust to thermal change (low confidence) (Bozinovic et
4 al., 2011; Overgaard et al., 2014; Magozzi and Calosi, 2015).
5
6 Physiological changes have observable impacts on morphology, such as changes to body size (and length of
7 appendages) and colour changes in butterflies, dragonflies, and birds (medium confidence) (Galeotti et al.,
8 2009; Karell et al., 2011), but trends are not always linear or consistent across realms, taxonomic groups or
9 geographic regions (Gotanda et al., 2015). Some morphological changes arise in response to environmental
10 changes rather than as the result of genetic adaptation or selection for an optimum body type. For example,
11 dietary changes associated with climate change have led to changes in chipmunk skull morphology (Walsh et
12 al., 2016).
13
14 Decreased body size has been suggested as a general response of species to climate change in freshwater
15 species given the temperature related constraints of metabolism with increasing body size. Reduced body
16 size in response to global warming has been documented for freshwater bacteria, plankton and fish, as well
17 as a shift towards smaller species (low confidence) (Daufresne et al., 2009; Winder et al., 2009; Jeppesen et
18 al., 2010; Crozier and Hutchings, 2014; Jeppesen et al., 2014; Farmer et al., 2015; Rasconi et al., 2015;
19 Woodward et al., 2016). However, the lack of systematic empirical evidence in freshwaters and confounding
20 effects such as interactions between temperature, nutrient availability and predation limit generalisations
21 about body size effects (Pomati et al., 2020 Nutrients).
22
23 Evidence is weak for a consistent reduction in body size across taxonomic groups in terrestrial animals (low
24 confidence) (Siepielski et al., 2019). Decreased body size in warmer climates (as higher surface area to
25 volume ratios maximise heat loss) is expected based on biogeographic patterns such as Bergmann’s Rule,
26 but both increases and decreases have been documented in mammals, birds, lizards and invertebrates and
27 attributed to climate change (Teplitsky and Millien, 2014; Gotanda et al., 2015; Gardner et al., 2019; Hill et
28 al., 2021). Contrasting patterns (increased body size) may be due to short-term modifications in selection
29 pressures (e.g. changes to predation and competition), variation in life histories or a result of interactions
30 with climate variables other than temperature (e.g. changes to food availability with rainfall changes) and
31 other disturbances (Yom-Tov and Yom-Tov, 2004; Gardner et al., 2019; Wilson et al., 2019) or body size
32 measurements (linear vs. volumetric dimensions) (Salewski et al., 2014).
33
34 Several lines of evidence suggest evolution of melanism in response to climate change (low confidence),
35 with colour changes associated with thermoregulation being demonstrated in butterflies (Zeuss et al., 2014;
36 MacLean et al., 2016; MacLean et al., 2019a), beetles (de Jong and Brakefield, 1998; Brakefield and de
37 Jong, 2011; Zvereva et al., 2019), dragonflies (Zeuss et al., 2014) and phasmids (Nosil et al., 2018). Such
38 changes may represent decreased phenotypic diversity and, potentially, genetic diversity (low confidence),
39 but the consequences of climate change on the genetic structure and diversity of populations have not been
40 widely assessed (Pauls et al., 2013). Simplistically, the thermal melanism hypothesis suggests that lighter
41 (higher reflectance) individuals should have increased fitness and therefore be selected for in a warmer
42 climate (Clusella-Trullas et al., 2007). However, several biotic (e.g thermoregulatory requirements, predator
43 avoidance, signalling) and abiotic (e.g. UV, moisture, interannual variability) factors interact to influence
44 changes in colour, making attribution to climate change across species and broad geographic regions difficult
45 (Kingsolver and Buckley, 2015; Stuart-Fox et al., 2017; Clusella-Trullas and Nielsen, 2020).
46
47 Interactions between morphological changes and changes to phenology may facilitate or constrain adaptation
48 to climate change (medium confidence) (Hedrick et al., 2021). For example, advancing phenology in
49 migratory species may impose selection on morphological traits (e.g. wing length) to increase migration
50 speed. If advancing spring phenology results in earlier breeding, this may offset the effect of rising
51 temperatures in the breeding range and reduce the effect of increasing temperature on body size (Zimova et
52 al., 2021). A study of 52 species of North American migratory birds, based on more than 70,000 specimens,
53 showed that spring migration phenology has advanced over the past 40 years, concurrent with widespread
54 shifts in morphology (reduced body size and increased wing length), perhaps to compensate for the increased
55 metabolic cost of flight as body size decreases (Weeks et al., 2020).
56
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1 A lack of understanding of physiological constraints and mechanisms remains a barrier to predicting many of
2 the ecological effects of climate change (Bozinovic et al., 2011; Vázquez et al., 2017; González-Tokman et
3 al., 2020). Many behavioural, morphological and physiological responses are highly species and context
4 specific, making generalisations difficult (Bodensteiner et al., 2021). Recent advances in mechanistic
5 understanding (from experiments), in process-based modeling that includes microclimates and
6 developmental processes (Carter and Janzen, 2021) and in sophistication of niche models (Kearney et al.,
7 2009) have improved projections, but comprehensive tests of geographic patterns and processes in thermal
8 tolerance and plasticity are still lacking, with studies limited to a few phylogenetically restricted analyses
9 showing mixed results (Gunderson and Stillman, 2015). Improved understanding of the mechanistic basis for
10 observed geographic patterns in thermal tolerance and plasticity is needed to identify species’ physiological
11 limits, the potential for adaptation and the presence of evolutionary trade-offs, which will strongly influence
12 population declines, species range shifts, invasive interactions and success of conservation interventions
13 (Cooke et al., 2021; Ryan and Gunderson, 2021).
14
15 2.4.2.7 Observed Impacts of Climate Change on Diseases of Wildlife and Associated Impacts on Humans
16
17 Assessment of changes in diseases of terrestrial and freshwater wild organisms was scarce in WGII AR4,
18 AR5, IPCC SR1.5 and IPCC SRCCL. Most emerging infectious diseases (EIDs) are zoonoses, that is,
19 transmissible between humans and animals, and climate sensitive (Woolhouse et al., 2001; Woolhouse and
20 Gowtage-Sequeria, 2005; McIntyre et al., 2017; Salyer et al., 2017). WGII AR4 found weak to moderate
21 evidence that disease vectors and their diseases had changed their distributions in concert with climate
22 change, but attribution studies were lacking. In WGII AR5 Chapter 11, geographic expansion of a few vector
23 borne diseases (VBDs) to higher latitudes and elevations had been detected and associated with regional
24 climate trends, but non-climatic drivers were not assessed well, leading to only a medium confidence in
25 attribution) (IPCC, 2014). Here we build upon previous assessments by focusing on changes in population
26 dynamics and geographic distributions of diseases of wildlife, and those of humans and domestic animals
27 that are also harbored, amplified, and transmitted by wild animal reservoir hosts and vectors.
28
29 Increased disease incidence is correlated with regional climatic changes and is expected from underlying
30 biology of relationships between temperature, precipitation, and disease ecology (robust evidence, high
31 agreement) (Norwegian Polar Institute, 2009; Tersago et al., 2009; Tabachnick, 2010; Paz, 2015; Dewage et
32 al., 2019; Deksne et al., 2020; Shocket et al., 2020; Couper et al., 2021). Whether increases in diseases in
33 wild and domestic animals correspond to increased disease risk in nearby human populations is complicated
34 by potential buffering effects of the local medical system, healthcare access, socio-economic status,
35 education, behaviours and general health of the human population (see also Chapter 7 and Cross-Chapter
36 Box ILLNESS this Chapter).
37
38 2.4.2.7.1 Direct effects of climate on reproduction, seasonality, growing season length and transmission of
39 pathogens, vectors, and hosts
40 VBDs require arthropod vector hosts (e.g., insects or ticks), while other infectious diseases (e.g., fungi,
41 bacteria, and helminths) have free-living life stages and/or complex life cycles that require intermediate hosts
42 (e.g., snails), all of which have temperature-driven rates of development and replication/reproduction (robust
43 evidence, high agreement) (Mordecai et al., 2013; Liu-Helmersson et al., 2014; Moran and Alexander, 2014;
44 Bernstein, 2015; Marcogliese, 2016; Ogden and Lindsay, 2016; Mordecai et al., 2017; Short et al., 2017;
45 Caminade et al., 2019; Cavicchioli et al., 2019; Mordecai et al., 2019; Liu et al., 2020; Rocklöv and Dubrow,
46 2020). Additionally, microbes such as bacteria thermally adapt to temperature changes through multiple
47 mechanisms, indicating that warming will not reduce antibiotic resistance (MacFadden et al., 2018; Pärnänen
48 et al., 2019; Shukla, 2019; McGough et al., 2020; Rodriguez-Verdugo et al., 2020).
49
50 There is increasing evidence for a role of extreme events in disease outbreaks (Tjaden et al., 2018; Bryson et
51 al., 2020). Heat waves have been associated with outbreaks of helminth pathogens, especially in subarctic
52 and Arctic areas. For example, a severe outbreak of microfilaremia, a vector-borne disease spread by
53 mosquitoes and flies, plagued reindeer in northern Europe following extreme high temperatures (Laaksonen
54 et al., 2010). More frequent and severe extreme events such as floods, droughts, heat waves, and storms can
55 either increase or decrease outbreaks, depending upon the region and disease (robust evidence, high
56 agreement) (Anyamba et al., 2001; Marcheggiani et al., 2010; Brown and Murray, 2013; Paz, 2015; Boyce et
57 al., 2016; Wu et al., 2016b; Wilcox et al., 2019; Nosrat et al., 2021). Heavy precipitation events have been
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1 shown to increase some infectious diseases with aquatic life cycle components such as mosquito-borne,
2 helminth, and rodent-borne diseases (robust evidence, high agreement) (Anyamba et al., 2001; Zhou et al.,
3 2005; Wu et al., 2008; Brown and Murray, 2013; Anyamba et al., 2014; Boyce et al., 2016). Conversely,
4 flooding also increases flow rate and decreases parasite load and diversity in other aquatic wildlife (Hallett
5 and Bartholomew, 2008; Bjork and Bartholomew, 2009; Marcogliese, 2016; Marcogliese et al., 2016) and
6 can reduce mosquito abundance by flushing them out of the system (Paaijmans et al., 2007; Paz, 2015).
7
8 Droughts reduce aquatic habitat of some mosquito species while simultaneously increasing the availability of
9 stagnant standing pools of water that are ideal breeding habitats for other species, such as dengue vector
10 Aedes mosquitos (medium evidence, medium agreement) (Chareonviriyaphap et al., 2003; Chretien et al.,
11 2007; Padmanabha et al., 2010; Trewin et al., 2013; Paz, 2015). Extreme drought has been associated with
12 an increase in bluetongue virus haemorrhagic disease in wildlife in eastern North America, though
13 mechanisms were not identified (Christensen et al., 2020). Heatwaves in some regions, especially coastal
14 regions, increased parasitism and decreased host richness and abundance leading to population crashes
15 (Larsen and Mouritsen, 2014; Mouritsen et al., 2018). Changes in temperature and precipitation, especially
16 extreme events, can alter community structure (Larsen et al., 2011) by increasing or decreasing parasites and
17 their host organisms and even altering host behavior in ways advantageous to parasites (Macnab and Barber,
18 2012).
19
20 Climate change not only affects the occurrence of pathogens and their hosts in geographic space but also the
21 temporal patterns of disease transmission. Warmer winters allow greater overwinter survival of arthropod
22 vectors which, coupled with lengthened tranmission seasons, drive increases in vector population sizes,
23 pathogen prevalence, and hence proportion of vectors infected (robust evidence, high agreement)
24 (Laaksonen et al., 2009; Molnár et al., 2013; Waits et al., 2018). For example, a parasitic nematode lung
25 worm (Umingmakstrongylus pallikuukensis) has shortened its larval development time in half (from two
26 years to one year), which has increased infection rates in North American muskoxen (Norwegian Polar
27 Institute, 2009).
28
29 Case study 1: Climate change impacts on pathogenic helminths in Europe
30
31 Parasitic helminth worms can reduce growth and yield, or kill livestock, and infect humans and wildlife,
32 leading to health, agricultural and economic losses (Fairweather, 2011; Charlier et al., 2016; Charlier, 2020).
33 Attribution of increased helminth disease incidence and risk to climate change is stronger than for most
34 human diseases because of long-term records and careful analysis of other anthropogenic drivers (e.g. land
35 use change, agricultural/livestock intensification, and antihelminthic intervention and resistance) (van Dijk et
36 al., 2008; van Dijk et al., 2010; Fox et al., 2011b; Martínez-Valladares et al., 2013; Charlier et al., 2016;
37 Innocent et al., 2017; Mehmood et al., 2017).
38
39 In Europe, evidence from laboratory studies, long term surveillance, statistical analyses, and modelling
40 shows that multiple helminth pathogens and their host snails have extended their transmission windows and
41 have increased survival, fecundity, growth and abundances (robust evidence, high agreement). Furthermore,
42 they have expanded or shifted their ranges poleward due to increases in temperature, precipitation and
43 humidity (robust evidence, high agreement ) (Lee et al., 1995; Pritchard et al., 2005; Poulin, 2006; van Dijk
44 et al., 2008; van Dijk et al., 2010; Fairweather, 2011; Fox et al., 2011b; Martínez-Valladares et al., 2013;
45 Bosco et al., 2015; Caminade et al., 2015; Caminade et al., 2019). These documented changes in climate,
46 hosts and pathogens have been linked to higher disease incidence and more frequent outbreaks in livestock
47 across Europe (high confidence) (Bosco et al., 2015).
48
49 Case study 2: Chytrid fungus and climate change
50
51 Infection by the chytrid fungus, Batrachochytrium dendrobatidis (Bd), can cause chytridiomycosis in
52 amphibians. Bd is widely distributed globally and has caused catastrophic disease in amphibians, associated
53 with declines of 501 species and extinctions of a further 90 species, primarily in tropical regions of the
54 Americas and Australia (Scheele et al., 2019; Fisher and Garner, 2020). Bd successfully travelled with high-
55 elevation Andean frog species as they expanded their elevational ranges upward, driven by regional
56 warming, to > 5200 m (Seimon et al., 2017).
57
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1 New findings since AR5 from controlled laboratory experiments (manipulating temperature, humidity and
2 water availability), intensive analyses of observed patterns of infection and disease in nature, and modeling
3 studies have led to an emerging consensus that interactions between chytrids and amphibians are climate-
4 sensitive, and that the interaction of climate change and Bd has driven many of the observed global
5 amphibian declines and species' extinctions (robust evidence, high agreement) (Rohr and Raffel, 2010;
6 Puschendorf et al., 2011; Rowley and Alford, 2013; Raffel et al., 2015; Sauer et al., 2018; Cohen et al.,
7 2019a; Sauer et al., 2020; Turner et al., 2021).
8
9 The "thermal mismatch hypothesis" posits that vulnerability to disease should be higher at warm
10 temperatures in cool-adapted species and higher at cool temperatures in warm-adapted species and is
11 generally supported. However, the most recent studies reveal more complex mechanisms underlying
12 amphibian-disease-climate change dynamics, including variation in thermal preferences among individuals
13 in a single amphibian population (robust evidence, high agreement) (Zumbado-Ulate et al., 2014; Sauer et
14 al., 2018; Cohen et al., 2019b; Neely et al., 2020; Sauer et al., 2020).
15
16 Bd is not universally harmful—it has been recorded as endemic in frog populations that did not suffer
17 disease, where it may be commensal rather than parasitic (Puschendorf et al., 2006; Puschendorf et al., 2011;
18 Rowley and Alford, 2013). Projections of future impacts are difficult, as the virulence of Bd is variable
19 across Bd populations and dependent upon the evolutionary and ecological histories, and evolutionary
20 potentials, of both the local amphibian populations and the endemic or invading Bd (robust evidence, high
21 agreement) (Retallick et al., 2004; Daskin et al., 2011; Puschendorf et al., 2011; Phillips and Puschendorf,
22 2013; Rowley and Alford, 2013; Zumbado-Ulate et al., 2014; Sapsford et al., 2015; Voyles et al., 2018;
23 Bradley et al., 2019; Fisher and Garner, 2020; McMillan et al., 2020). Further, specific local habitats might
24 serve as regional climate refugia from chytrid infection (e.g. hot and dry) (medium evidence, high
25 agreement) (Zumbado-Ulate et al., 2014; Cohen et al., 2019b; Neely et al., 2020; Turner et al., 2021).
26
27
28 2.4.2.7.2 Effects on geographic distribution and connectivity patterns of pathogens
29 As species’ geographic ranges and migration patterns are modified by climate change (Section 2.4.2.1, Table
30 2.2), pathogens accompany them. Diverse vectors and associated parasites, pests, and pathogens of plants
31 and animals are being recorded at higher latitudes and elevations in conjunction with regional temperature
32 increases and precipitation changes (robust evidence, high agreement), although analysis of realized disease
33 incidence often lacks inclusion of non-climatic vs climate drivers, compromising attribution (Ollerenshaw
34 and Rowlands, 1959; Purse et al., 2005; Laaksonen et al., 2010; van Dijk et al., 2010; Alonso et al., 2011;
35 Genchi et al., 2011; Pinault and Hunter, 2011; Jaenson et al., 2012; Loiseau et al., 2012; Kweka et al., 2013;
36 Medlock et al., 2013; Dhimal et al., 2014a; Dhimal et al., 2014b seasonal; Siraj et al., 2014; Khatchikian et
37 al., 2015; Hotez, 2016a; Hotez, 2016b; Bett et al., 2017; Mallory and Boyce, 2017; Strutz, 2017; Booth,
38 2018; Dumic and Severnini, 2018; Carignan et al., 2019; Gorris et al., 2019; Le et al., 2019; Stensgaard et
39 al., 2019b snails and; Brugueras et al., 2020; Gilbert, 2021).
40
41 At least six major VBDs affected by climate drivers have recently emerged in Nepal and are now considered
42 endemic, with climate change implicated as a primary driver as LULCC has been assessed to have a minimal
43 influence on these diseases (high confidence) (Table SM2.1). There is increasing evidence that climate
44 warming has extended the elevational distribution of Anopheles, Culex and Aedes mosquito vectors above
45 2,000 m in Nepal (limited evidence, high agreement) (Dahal, 2008; Dhimal et al., 2014a; Dhimal et al.,
46 2014b; Dhimal et al., 2015) with similar trends being recorded in neighboring Himalayan regions (medium
47 evidence, high agreement) (Phuyal et al., 2020; Dhimal et al., 2021). Host animals in novel areas may be
48 immunologically naive, and therefore more vulnerable to severe illness (Bradley et al., 2005; Hall et al.,
49 2016).
50
51 Case study 3: Arctic and subarctic disease expansion and intensification
52
53 High Arctic regions have warmed by more than double the global average, >2°C in most areas (see Sections
54 2.3.1.1.2, Figure 2.11, and Atlas 11.2.1.2, in WGI). Experimental, field ecology studies and computational
55 models in Arctic and subarctic regions indicate that milder winters have reduced mortality of vectors and
56 reservoir hosts and increased their habitat as forested taiga expands into previously treeless tundra (Table
57 SM2.1; Parkinson et al., 2014). Warmer temperatures and longer seasonal windows have allowed faster
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1 reproduction/replication, accelerated development, and increased the number of generations per year of
2 pathogens, vectors and some host animals, that in turn increase the populations of disease organisms and
3 disease transmission (Sections 2.4.2.4, 2.4.4.3.3). Ticks, mosquitos, culicoides biting midges, deer flies,
4 horseflies, and simuliid black flies that transmit a variety of pathogens are being documented in high-latitude
5 regions at higher numbers or where they have been historically absent (robust evidence, high agreement)
6 (Waits et al., 2018; Caminade et al., 2019; Gilbert, 2021). In concert with these poleward shifts of hosts and
7 vectors, pathogens, particularly tick-borne pathogens and helminth infections, have increased dramatically in
8 incidence and severity from once rare occurrences and have appeared in new regions (robust evidence, high
9 agreement, very high confidence) (Caminade et al., 2019; Gilbert, 2021).
10
11 Zoonoses and VBDs that have been historically rare or never documented in Arctic and subarctic regions of
12 Europe, Asia, and North America, such as anthrax, cryptosporidiosis, elaphostrongylosis, filariasis (Huber et
13 al., 2020), tick-borne encephalitis, and tularemia (Evander and Ahlm, 2009; Parkinson et al., 2014; Pauchard
14 et al., 2016), are spreading poleward and increasing in incidence (robust evidence, high agreement, very high
15 confidence) (Table SM2.1; Omazic et al., 2019). Recent anthrax outbreaks and mass mortality events among
16 humans and reindeer, respectively, have been linked to abnormally hot summer temperatures that caused
17 permafrost to melt and exposed diseased animal carcasses, releasing thawed, highly infectious, Bacillus
18 anthracis spores (medium evidence, medium agreement) (Ezhova et al., 2019; Hueffer et al., 2020; Ezhova et
19 al., 2021). Multiple contributing factors conspired over different time scales to compound a 2016 anthrax
20 outbreak occurring on the Yamal peninsula: (i) rapid permafrost thawing for 5 years preceding the outbreak;
21 (ii) thick snow cover the year before the outbreak insulated the warmed permafrost and kept it from re-
22 freezing; and (iii) anthrax vaccination rates had decreased or ceased in the region (Ezhova et al., 2019;
23 Ezhova et al., 2021). These precursors converged with an unusually dry and hot summer that: (i) melted
24 permafrost, creating an anthrax exposure hazard; (ii) increased the vector insect population; and (iii)
25 weakened the immune systems of reindeer thus increasing their susceptibility (Waits et al., 2018; Hueffer et
26 al., 2020).
27
28 Warmer temperatures have increased blood-feeding insect harassment of reindeer with compounding
29 consequences: (1) increased insect bite rates lead to higher parasite loads, (2) time spent by raindeer in trying
30 to escape biting flies reduces foraging while simulataneously increasing energy expenditure, (3) the
31 combination of (1) and (2) lead to poor body condition, that subsequently leads to (4) reduced winter
32 survival and fecundity (Mallory and Boyce, 2017). As temperatures warm and connectivity increases
33 between the Arctic and the rest of the world, tourism, resource extraction, and increased commercial
34 transport will create additional risks of biological invasion by infectious agents and their hosts (Pauchard et
35 al., 2016). These increases in introduction risk compounded with climate change have already begun to harm
36 indigenous peoples dependent on hunting and herding livestock (horses and reindeer) that are suffering
37 increased pathogen infection (Deksne et al., 2020; Stammler and Ivanova, 2020).
38
39 2.4.2.7.3 Biodiversity-disease links
40 Anthropogenic impacts, such as disturbances caused by climate change, can reduce biodiversity through
41 multiple mechanisms and increase disease risk to humans (limited evidence, low agreement) but more
42 research is needed to understand the underlying mechanisms (Civitello et al., 2015; Young et al., 2017b;
43 Halliday et al., 2020; Rohr et al., 2020; Glidden et al., 2021). Known wildlife hosts of human-shared
44 pathogens and parasites overall comprise a greater proportion of local species richness (18–72% higher) and
45 abundance (21–144% higher) in sites under substantial human use (agricultural and urban lands) compared
46 with nearby undisturbed habitats (Gibb et al., 2020).
47
48 Exploitation of wildlife and degradation of natural habitats have increased opportunities for ‘spill over’ of
49 pathogens from wildlife to human populations and increased emergence of zoonotic disease epidemics and
50 pandemics (robust evidence, high agreement); animal and human migrations driven by climate change have
51 added to this increased risk (medium evidence, medium agreement) (see Section 2.4.2.1, Chapter 8, Cross-
52 Chapter Box MOVING PLATE in Chapter 5; Patz et al., 2004; Cleaveland et al., 2007; Karesh et al., 2012;
53 Altizer et al., 2013; Allen et al., 2017; Plowright et al., 2017; Olivero et al., 2017; Faust et al., 2018; Carlson
54 et al., 2020; Gibb et al., 2020; Hockings et al., 2020; IPBES, 2020; Volpato et al., 2020; Glidden et al.,
55 2021). Agricultural losses and subsequent food scarcity, that is increasing due to climate change, can also
56 lead to an increase in the use of bushmeat, and, hence increase risk of diseases jumping from wild animals to
57 humans (medium evidence, high agreement) (Brashares et al., 2004; Leroy et al., 2004; Wolfe et al., 2004;
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1 Rosen and Smith, 2010; Kurpiers et al., 2016).
2
3 2.4.2.7.4 Implications for humans of changes in diseases in wild animals
4 Changes in temperature, precipitation, humidity, and extreme events have been associated with more
5 frequent disease outbreaks, increases in disease incidence and severity, and novel disease and vector
6 emergence into new areas for wild animals, with a mechanistic understanding of the roles of these drivers
7 from experimental studies providing high confidence for the role of climate change. However, attribution of
8 how this has impacted human infectious diseases remains difficult, and definitive attribution studies are
9 lacking. The specific role of recent climate change is difficult to examine in isolation for most regions where
10 human disease incidence has also been affected by land use change (particularly agricultural and urban
11 expansion), changes in public health access and measures, socio-economic changes, increased global
12 movements of people, and changes in vector and rodent control programs, supporting medium confidence in
13 the role of climate change driving observed changes in human diseases globally. Exceptions are in areas
14 noted above (Arctic, subarctic, and high elevation regions), in which climate change fingerprints are strong
15 and/or concurrent changes in non-climatic drivers are less pronounced than in other regions (high confidence
16 for climate change attribution) (see Table SM2.1, Sections 5.5.1.3, 7.2.2.1, Cross-Chapter Box ILLNESS
17 this Chapter; Harvell et al., 2002; Norwegian Polar Institute, 2009; Tersago et al., 2009; Tabachnick, 2010;
18 Altizer et al., 2013; Garrett et al., 2013; Paz, 2015; Wu et al., 2016b; Caminade et al., 2019; Dewage et al.,
19 2019; Coates and Norton, 2020; Deksne et al., 2020; Shocket et al., 2020; Couper et al., 2021; Gilbert,
20 2021).
21
22
23 [START FAQ2.2 HERE]
24
25 FAQ2.2: How does climate change increase the risk of diseases?
26
27 Climate change is contributing to the spread of diseases in both wildlife and humans. Increased contact
28 between wildlife and human populations increases disease risk and climate change is altering where
29 pathogens that cause diseases and the animals that carry them live. Disease risk can often be reduced by
30 improving health care and sanitation systems, training the medical community to recognize and treat
31 potential new diseases in their region, limiting human encroachment into natural areas, limiting wildlife
32 trade, and promoting sustainable and equitable socioeconomic development.
33
34 Diseases spread between humans and animals are called zoonoses. Zoonoses comprise nearly two-thirds of
35 known human infectious diseases and the majority of newly emerging infectious diseases (EIDs). COVID-19
36 is the most recent zoonosis and has killed millions of people globally while devastating economies. The risk
37 posed by EIDs has increased because of: (1) movement of wild animals and their parasites into new areas via
38 climate change, global trade, and travel; (2) human intrusion into and conversion of natural areas for
39 agriculture, livestock, industrial/raw materials extraction, and housing; (3) increased wildlife trade and
40 consumption; (4) increased human mobility resulting from global trade, war/conflicts, and migration made
41 faster and farther by fossil fuel powered travel; and (5) widespread antimicrobial use, which can promote
42 antibiotic resistant infections (Figure FAQ2.3.1).
43
44
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1
2 Figure FAQ2.2.1: How diseases move from the wild into human populations. Climate change may increase diseases in
3 nature, but whether or not this leads to an increase in disease risk for humans depends upon a range of societal,
4 infrastructure and medical buffers that form a shield protecting humans.
5
6
7 Climate change further increases risk by altering pathogen and host animal (1) geographic ranges and
8 habitats; (2) survival, growth, and development; (3) reproduction and replication; (4) transmission and
9 exposure (5) behavior; and (6) access to immunologically naïve animals and people who lack infection
10 resistance. This can lead to novel disease emergence in new places, more frequent and larger outbreaks, and
11 longer or shifted seasons of transmission. Climate change is making it possible for many EIDs to colonize
12 historically colder areas that are becoming warmer and wetter in temperate and polar regions and in
13 mountains. Vector-borne diseases (VBDs) are diseases spread by vectors such as mosquitoes, sand flies,
14 kissing bugs, and ticks. For example, ticks that carry the virus that causes tick-borne encephalitis have
15 moved into northern subarctic regions of Asia and Europe. Viruses like dengue, chikungunya, and Japanese
16 encephalitis are emerging in Nepal in hilly and mountainous areas. Novel outbreaks of Vibrio bacteria
17 seafood poisoning are being traved to the the Baltic States and Alaska where they were never documented
18 before. Many scientific studies show that infectious disease transmission and the number of individuals
19 infected depends on rainfall and temperature; climate change often makes these conditions more favourable
20 for disease transmission.
21
22 Climate change can also have complicated, compounding, and contradictory effects on pathogens and
23 vectors. Increased rainfall creates more habitat for mosquitoes that transmit diseases like malaria but too
24 much rain washes away the habitat. Decreased rainfall also increases disease risk when people without
25 reliable water access use containers to store water in that mosquitos, such as the vectors of dengue fever,
26 Aedes aegypti and Ae. albopictus, use for egg laying. Hotter temperatures also increase mosquito bite rate,
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1 parasite development, and viral replication! Certain species of snails are intermediate hosts for many
2 helminth worm parasites that make humans, livestock, and wild animals sick. When it gets hot, the snails can
3 produce two to three times as many infective larvae but if it becomes too hot many pathogens and their
4 vectors cannot survive or reproduce.
5
6 Humans also contract zoonoses directly through their skin, mucus membranes, and lungs when eating or
7 butchering animals, or coming into contact with pathogen shed in the air, urine, or faeces that contaminates
8 water, food, clothing, and other surfaces. Any activity that increases contact with wildlife, especially in high
9 biodiversity regions like the tropics and subtropics, increases disease risk. Climate change-related disease
10 emergence events are often rare but may become more frequent. Fortunately, there are ways to reduce risks
11 and protect our health, as described below.
12
13 Habitat and biodiversity protection: Encroachment of humans into natural areas, due to expansion of
14 agriculture and livestock, timber harvests, resource extraction, and urban development has increased human
15 contact with wild animals, and creates more opportunities for disease spillover (transmission from an animal
16 to a new species, including humans). By conserving, protecting, and restoring wild habitats, we can build
17 healthier ecosystems that provide other services, such as clean air, clean and abundant water, recreation,
18 spiritual value, and well-being, as well as reduced disease spillover. If humans must go into wild areas or
19 hunt, they should take appropriate precautions such as wearing protective clothing, using insect repellant,
20 performing body checks for vectors like ticks, and washing hands and clothing well.
21
22 Food resilience: Investing in sustainable agroecological farming will alleviate the pressure to hunt wild
23 animals and reduce the conversion of more land to agriculture/livestock use. Stopping illegal animal trading
24 and poaching and decreasing reliance on wild meats and products made from animal parts will reduce direct
25 contact with potentially infected animals. This has the added benefit of increasing food security, nutrition,
26 improving soil, reducing erosion, preserving biodiversity, and mitigating climate change.
27
28 Disease prevention and response: The level of protection against infection is linked directly to the level of
29 development and wealth of a country. Improved education, high-quality medical and veterinary systems,
30 high food security, proper sanitation of water and waste, high housing quality, and disease surveillance and
31 alarm systems dramatically reduce disease risk and improve health. Utilizing a One Biosecurity or One
32 Health framework further improves resilience. Sharing knowledge within communities, municipalities,
33 regional, and between national health authorities globally is important to assessing, preventing and
34 responding to outbreaks and pandemics more efficiently and economically.
35
36 Humans are facing many direct or indirect challenges because of climate change. Increasing EIDs is one of
37 our greatest challenges, due to our ever-growing interactions with wildlife and the climatic changes creating
38 new disease transmission patterns. COVID-19 is a current crisis, and follows other recent EIDs: SARS,
39 HIV/AIDS, H1N1 influenza, Ebola, Zika, and West Nile fever. EIDs have accelerated in recent decades,
40 making it clear that new societal and environmental approaches to wildlife interactions, climate change, and
41 health are urgently needed to protect our current and future well-being as a species.
42
43 [END FAQ2.2 HERE]
44
45
46 2.4.2.8 Observed Evolutionary Responses to Climate Change
47
48 Prior sections document species' tendencies to retain their climate envelopes by some combination of range
49 shift and phenological change. However, this tracking of climate change can be incomplete, causing species
50 or populations to experience hotter conditions than those to which they are adapted and thereby incur
51 'climate debts' (Devictor et al., 2012). The importance of population-level debt is illustrated by a study in
52 which estimated debt values were correlated with population dynamic trends in a North American migratory
53 songbird, the Yellow Warbler, Setophaga petechia. Populations that were genetic outliers for their local
54 climate space had larger population declines (greater debt) than populations with genotypes closer to the
55 average values for that particular climate space. Debt values were estimated from genomic analyses
56 independent of the population trends, and were distributed across the species' range in a mosaic, not simply
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1 concentrated at range margins, rendering the results robust to being confounded by broad-scale geographical
2 trends (Bay et al., 2018).
3
4 In the absence of evolutionary constraints, climate debts can be cancelled by genetically-based increases in
5 thermal tolerance and ability to perform in high ambient temperatures. In species already showing local
6 adaptation to climate, populations currently living at relatively cool sites should be able to evolve to adopt
7 traits of populations currently at warmer sites, as their local experience of climate changes (Singer, 2017;
8 Socolar et al., 2017).
9
10 An increasing number of studies document evolutionary responses to climate change in populations not at
11 warm range limits (Franks and Hoffmann, 2012). Organisms with short generation times should have higher
12 capacity to genetically track climate change than species with long generation times, such as mammals
13 (Boutin and Lane, 2014). Indeed, observed evolutionary impacts have been mainly documented in insects,
14 especially at expanding range margins (Chuang and Peterson, 2016) where evolutionary changes have been
15 documented of dispersal ability (Thomas et al., 2001) and host specialisation (Bridle et al., 2014; Lancaster,
16 2020).
17
18 Away from range margins, individual populations experiencing regional warming have evolved diverse traits
19 related to climate adaptation. For example, pitcher-plant mosquitos (Wyeomyia smithii) in Pacific NW
20 America have evolved to wait for shorter day lengths before initiating diapause. This adaptation to
21 lengthening summers enables them to delay overwintering until later and add an extra generation each year
22 (Bradshaw and Holzapfel, 2001). Among 26 populations of Drosophila subobscura studied on three
23 continents, 22 experienced climate warming across two or more decades, and 21 of those 22 showed
24 increasing frequencies of chromosome inversions characteristic of populations adapted to hot climates
25 (Balanya et al., 2006).
26
27 However, for populations already at their warm range limits, their ability to track climate change in situ
28 would require evolving to survive and reproduce outside their species' historical climate envelope, which is
29 not supported by experimental or observational evidence (medium evidence, high agreement) (Singer, 2017).
30 Whether or not they can do so depends on the level of 'niche conservatism' operating at the species level
31 (Lavergne et al., 2010). If a species' whose range limits are determined by climate finds itself completely
32 outside of its traditional climate envelope, extinction is expected in the absence of 'evolutionary rescue' (Bell
33 and Gonzalez, 2009; Bell et al., 2019). To investigate the evolutionary potential enabling a species to survive
34 in a novel climate entirely outside its traditional climate envelope, experiments have been carried out on
35 ectotherms testing thermal performances, thermal tolerances, and their evolvabilities (Castaneda et al., 2019;
36 Xue et al., 2019). Tests of thermal performance have been complicated as both long-term acclimation and
37 transgenerational effects occur (Sgro et al., 2016). However, the results to date have been consistent: despite
38 widespread local adaptation to climate across species' ranges, substantial constraints exist to the evolution of
39 greater stress tolerance (e.g. high temperatures and drought) at warm range limits (medium evidence, high
40 agreement) (Hoffmann and Sgro, 2011; MacLean et al., 2019b). For example, as temperature was
41 experimentally increased, the amount of genetic variance in fitness of Drosophila melanogaster decreased:
42 in hot environments, flies had low evolvability (Kristensen et al., 2015). The hypothesis that heat stress
43 tolerance is evolutionarily constrained is further supported by experiments in which 22 Drosophila species
44 drawn from tropical and temperate climes were subjected to extremes of heat and cold. They differed as
45 expected in cold tolerances, but not in heat tolerances nor in temperatures at which optimal performances
46 were observed (MacLean et al., 2019b).
47
48 Plasticity in acclimating to thermal regimes helps organisms adapt to environmental change. The form and
49 extent of plasticity can vary among populations experiencing different climates (Kelly, 2019) and generate
50 phenotypic values outside the prior range for the species, but plasticity itself has not yet been observed to
51 evolve in response to climate change (Kelly, 2019). Relevant genetic changes in nature (e.g. affecting heat
52 tolerance) have not yet been shown to alter the boundaries of existing genetic variation for any species.
53 Evolutionary rescue of entire species has not yet been observed in nature, nor is it expected based upon
54 experimental and theoretical studies (medium evidence, high agreement).
55
56 Hybridisation between closely related species has increased in recent decades as one species shifts its range
57 boundaries and positions itself more closely to the other—hybrids between polar bears and brown bears have
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1 been documented in northern Canada (Kelly et al., 2010). In North American rivers, hybridisation between
2 invasive rainbow trout and native cutthroat trout has increased in frequency as the rainbow trout expanded
3 into warming waters (Muhlfeld et al., 2014). Whether climate-changed induced hybridisations can generate
4 novel climate adaptations remains to be seen.
5
6 In summary, with present knowledge, evolution is not expected to be sufficient to prevent whole species'
7 extinctions if a species' climate space disappears (high confidence).
8
9 2.4.3 Observed Changes in Key Biomes, Ecosystems and their Services
10
11 2.4.3.1 Detection and Attribution for Observed Biome Shifts
12
13 Attribution for biome (major vegetation form of an ecosystem) shifts is complex because of their extensive,
14 sometimes continental, spatial scale (Whittaker, 1975; Olson et al., 2001; Woodward et al., 2004); and
15 therefore, non-climatic factors strongly influence biome spatial distributions (Ellis and Ramankutty, 2008).
16
17 The most robust attribution studies use data from individual locations with minimal confounding factors,
18 particularly recent land use change, and scale up by analysing multiple locations across a long zone between
19 biomes. As with individual species, multiple lines of evidence increase confidence (Hegerl et al., 2010;
20 Parmesan et al., 2013). Multivariate statistical analyses aid attribution studies by allowing the assessment of
21 relative weights among multiple factors, including variables related to climate change (Gonzalez et al.,
22 2012). However, drivers often have strong, significant interactions with one another, complicating
23 quantitative assessment of the strength of individual drivers (Parmesan et al., 2013). In these cases,
24 manipulative experiments are critical in assessing attribution to climate change drivers.
25
26 Certain biomes exhibit a relatively stronger relationship to climate; for example, Arctic tundra generally has
27 a distinct ecotone with boreal conifer forest (Whittaker, 1975). In these areas, attribution of biome shifts to
28 climate change are relatively straightforward, if human land use change is minimal. However, other biomes,
29 such as many grassland systems, are not at equilibrium with climate (Bond et al., 2005). In these systems
30 their evolutionary history (Keeley et al., 2011; Strömberg, 2011; Charles-Dominique et al., 2016),
31 distribution, structure and function have been shaped by climate and natural disturbances, such as fire and
32 herbivory (Staver et al., 2011; Lehmann et al., 2014; Pausas, 2015; Bakker et al., 2016; Malhi et al., 2016).
33 Disturbance variability is an inherent characteristic of grassland systems and suitable “control” conditions
34 are seldom available in nature. Furthermore, due to the integral role of disturbance, these biomes have been
35 widely affected by long-term and widespread shifts in grazing regimes, large-scale losses of mega-
36 herbivores and fire suppression policies (Archibald et al., 2013; Malhi et al., 2016; Hempson et al., 2017). It
37 is necessary to conduct climate change attribution on a case-by-case basis for grasslands; such assessments
38 are complex as direct climate change impacts from either inherent variation within disturbance regimes or
39 directional changes in background disturbances are difficult to separate (detailed in Sections 2.4.3.2.1;
40 2.4.3.2.2; 2.4.3.5). Confidence in assessments is increased when observed trends are supported by
41 mechanistic understanding of responses identified by physiological studies, manipulative field experiments,
42 greenhouse studies and lab experiments (Table SM2.1).
43
44 2.4.3.2 Global Patterns of Observed Biome Shifts Driven by Climate Change
45
46 2.4.3.2.1 Observed biome shifts predominantly driven by climate change
47 Th IPCC Fifth Assessment Report and a meta-analysis found that vegetation at the biome level shifted
48 poleward latitudinally and upward altitudinally due to anthropogenic climate change at 19 sites in boreal,
49 temperate, and tropical ecosystems from 1700 to 2007 (Gonzalez et al., 2010a; Settele et al., 2014). In these
50 areas, temperature increased 0.4° to 1.6ºC above the pre-industrial period (Gonzalez et al., 2010a; Settele et
51 al., 2014). Field research since the IPCC Fifth Assessment Report detected additional poleward and upslope
52 biome shifts over periods of 24 to 210 years at numerous sites (described below) but were not directly
53 attributed to anthropogenic climate change as the studies were not designed nor conducted properly for
54 attribution.
55
56 Many of the recently detected shifts were nevertheless consistent with climate change temperature increases
57 and observed in areas lacking agriculture, livestock grazing, timber harvesting, or other anthropogenic land
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1 uses. For example, in the Andes Mountains in Ecuador, a biome shift was detected by comparing a survey by
2 Alexander von Humboldt in 1802 to a re-survey in 2012, making this the longest time span in the world for
3 this type of data (Morueta-Holme et al., 2015) and 2017 (Moret et al., 2019). During 210 years, temperature
4 increased 1.7ºC (Morueta-Holme et al., 2015) and the upper edge of alpine grassland shifted upslope 100‒
5 450 m (Moret et al., 2019).
6
7 Other biome shifts consistent with climate change and not substantially affected by local land use include
8 northward shifts of deciduous forest into boreal conifer forest in Canada (5 km between 1970-2012, (Sittaro
9 et al., 2017) and 20 km between 1970-2014, (Boisvert-Marsh et al., 2019)) and northward shifts of
10 temperate conifer into boreal conifer forest in Canada (21 km between 1970-2015, (Boisvert-Marsh and de
11 Blois, 2021)). Research detected upslope shifts of boreal and sub-alpine conifer forest into alpine grassland
12 at 143 sites on four continents (41 m, 1901-2018, (Lu et al., 2021)) and individual sites in Canada (54 m,
13 1900-2010, (Davis et al., 2020)), China (300 m, 1910-2000 (Liang et al., 2016); 33 m, 1985-2014, (Du et
14 al., 2018)), Nepal (50 m, 1860-2000, (Sigdel et al., 2018)), Russia (150 m, 1954-2006, (Gatti et al., 2019))
15 and the United States (19 m, 1950-2016, (Smithers et al., 2018); 38 m, 1953-2015, (Terskaia et al., 2020)).
16 Other upslope cases include shifts of temperate conifer forest in Canada (Jackson et al., 2016) and the United
17 States (Lubetkin et al., 2017), temperate deciduous forest in Switzerland (Rigling et al., 2013) and temperate
18 shrubland in the United States (Donato et al., 2016).
19
20 In summary, anthropogenic climate change has caused latitudinal and elevational biome shifts in at least 19
21 sites in boreal, temperate, and tropical ecosystems between 1700 and 2007, where temperature increased 0.4°
22 to 1.6ºC above the pre-industrial period (robust evidence, high agreement). Additional cases of 5 to 20 km
23 northward and 20 to 300 m upslope biome shifts between 1860 and 2016, under approximately 0.9ºC mean
24 global temperature increase above the pre-industrial period, are consistent with climate change (medium
25 evidence, high agreement).
26
27
28 2.4.3.2.2 Observed biome shifts from combined land use change and climate change
29 Research has detected biome shifts in areas where agriculture, fire use or suppression, livestock grazing,
30 timber and fuelwood harvesting, or other local land use actions substantially altered vegetation, in addition to
31 changes in climatic factors and CO2 fertilisation. These studies were not designed or conducted in a manner
32 to make climate change attribution possible, although vegetation changes are consistent with climate change:
33 for example, a global review of observed changes in treelines found that 2/3 of treelines globally have
34 shifted upslope in elevation over the past 50 years or more (Hansson, 2021, a review of).
35
36 Upslope and poleward forest shifts have occurred where timber harvesting or livestock grazing was
37 abandoned, allowing regeneration of trees at sites in Canada (Brice et al., 2019; Wang et al., 2020b), France
38 (Feuillet et al., 2020), Italy (Vitali et al., 2017), Spain (Ameztegui et al., 2016), the United States (Wang et
39 al., 2020b) and mountain areas across Europe (Cudlin et al., 2017). Intentional use of fire drove an upslope
40 forest shift in Peru (Bush et al., 2015) while mainly human-ignited fires drove conversion of shrubland to
41 grassland in a drought-affected area of the United States (Syphard et al., 2019b). In eastern Canada, timber
42 harvesting and wildfire drove conversion of mixed conifer-broadleaf forests to broadleaf-dominated forests
43 (Brice et al., 2020; Wang et al., 2020b).
44
45 Shrub encroachment onto savanna has occurred at numerous sites, particularly across the Southern
46 Hemisphere, mainly between 1992 and 2010 (Criado et al., 2020). Globally, overgrazing initiates shrub
47 encroachment by reducing grasses more than woody plants, while fire exclusion maintains the shrub cover
48 (D'Odorico et al., 2012; Caracciolo et al., 2016; Bestelmeyer et al., 2018). The magnitude of woody cover
49 change in savannas is not correlated to mean annual temperature change (Criado et al., 2020), however,
50 higher atmospheric CO2 increases shrub growth in savannas (Nackley et al., 2018; Manea and Leishman,
51 2019). A global remote sensing analysis of biome changes from all causes, including agricultural and grazing
52 expansion and deforestation, estimated that 14% of pixels changed between 1981 and 2012, although this
53 approach can overestimate global changes since it uses a new biome classification system, which doubles the
54 conventional biome classifications (Higgins et al., 2016). In addition to climate change, land use change
55 causes vegetation changes at the biome level (robust evidence, high agreement).
56
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1 2.4.3.3 Observed Changes in Deserts and Arid Shrublands
2
3 Divergent responses to anthropogenic climate change are occurring within and across arid regions,
4 depending on time period, location, detection methodology and vegetation type (see Cross-Chapter Paper 3).
5 Emerging shifts in ecosystem structure, functioning and biodiversity are supported by evidence from
6 modelled impacts of projected climate and CO2 levels. While observed responsiveness of arid vegetation
7 productivity to rising atmospheric CO2 (Fensholt et al., 2012b) may offset risks from reduced water
8 availability (Fang et al., 2017), climate- and CO2 driven changes are key risks in arid regions, interacting
9 with habitat degradation, wildfire, and invasive species (Hurlbert et al., 2019).
10
11 Widespread vegetation greening, as projected in AR4, is occurring in arid shrublands (Zhang et al., 2019a;
12 Maestre et al., 2021) as a result of increases in leaf area, woody cover and herbaceous production at desert-
13 grassland interfaces (Gonsamo et al., 2021). Plant productivity in arid regions has increased (Fensholt et al.,
14 2012b) because of improved water use efficiency associated with elevated CO2 (Norby and Zak, 2011;
15 Donohue et al., 2013; Burrell et al., 2020; Gonsamo et al., 2021) (medium evidence, high agreement), altered
16 rainfall seasonality and amount (Rohde et al., 2019; Zhang et al., 2019a ) (robust evidence, high agreement),
17 increases in temperature (Ratajczak et al., 2014; Wilcox et al., 2018)(robust evidence, high agreement) and
18 heavy grazing (robust evidence, high agreement) with relative importance differing among locations
19 (Donohue et al., 2013; Caracciolo et al., 2016; Archer et al., 2017; Hoffmann et al., 2019b; Rohde et al.,
20 2019). Woody plant encroachment into arid shrublands is occurring in North America (Caracciolo et al.,
21 2016; Archer et al., 2017), southern Africa (du Toit and O’Connor, 2014; Ward et al., 2014; Masubelele et
22 al., 2015a; Hoffman et al., 2019; Rohde et al., 2019) (high confidence) and Central Asia (Li et al., 2015) (low
23 confidence). In North America, sagebrush steppe changes have been attributed to increases in temperature
24 and earlier snowpack melt (Wuebbles et al., 2017; Mote et al., 2018; Snyder et al., 2019).
25
26 Non-native grasses are invading the sagebrush steppes (cold deserts) in North America (Chambers et al.,
27 2014) attributed to warming (Bradley et al., 2016; Hufft and Zelikova, 2016). In the eastern semi-desert
28 (Karoo) of South Africa, annual rainfall increases and a rainfall seasonality shift (du Toit and O’Connor,
29 2014) are increasing grassiness as arid grasslands expand into semi-desert shrublands (du Toit et al., 2015;
30 Masubelele et al., 2015b; Masubelele et al., 2015a) causing fire in areas seldom burned (Coates et al., 2016).
31
32 Drought, warming, and land management interactions have caused vegetation mortality (see section 2.4.4.3)
33 and reduced vegetation cover in shrublands as projected by AR4 (Burrell et al., 2020). Increased heat and
34 drought are causing succulent species health and abundance to decline (Musil et al., 2009; Schmiedel et al.,
35 2012; Aragón-Gastélum et al., 2014; Koźmińska et al., 2019). Hot droughts especially reduce population
36 resilience (medium confidence) (Koźmińska et al., 2019).
37
38 2.4.3.4 Observed Changes in Mediterranean-Type Ecosystems
39
40 Since AR5, Settele et al. (2014) found that all five Mediterranean-Type Ecosystems (MTEs) of the world
41 experienced extreme droughts within the past decade, with South Africa and California reporting the worst
42 on record (robust evidence, high agreement) (Diffenbaugh et al., 2015; Williams et al., 2015a; Garreaud et
43 al., 2017; Otto et al., 2018; Sousa et al., 2018). Climate change is causing these droughts to become more
44 frequent and severe (medium evidence, medium agreement) (AghaKouchak et al., 2014, Garreaud et al 2017
45 The 2010-2015 megadrought , AR6 WGI Chpt 11; Otto et al., 2018).
46
47 MTEs show a range of direct responses to various forms of water deficit, but have also been affected by
48 increasing fire activity linked to drought (Abatzoglou and Williams, 2016), and interactions between drought
49 or extreme weather and fire, affecting post-fire ecosystem recovery (Slingsby et al., 2017). Responses
50 include shifts in functional composition (Acácio et al., 2017; Syphard et al., 2019a), decline in vegetation
51 health (Hope et al., 2014; Asner et al., 2016a), decline or loss of characteristic species (White et al., 2016;
52 Stephenson et al., 2019), shifts in composition towards more drought- or heat-adapted species and declining
53 diversity (also see Section 2.4.4.3; Slingsby et al., 2017.; Harrison et al., 2018).
54
55 Declines in plant health and increased mortality in MTEs associated with drought have been widely
56 documented (robust evidence, high agreement) (Section 2.4.4.3). Remote sensing studies show drought
57 associated mortality in postfire vegetation regrowth in the Fynbos of South Africa (Slingsby et al., 2020b),
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1 reduced canopy health in forests within MTE zones of South Africa (Hope et al., 2014), and declines in
2 canopy water content in forests of California (Asner et al., 2016a). Several studies reported climate-
3 associated responses of dominant or charismatic species. High mortality in the Clanwilliam Cedar between
4 1931 to 2013 occurred at lower, hotter elevations in the Fynbos of South Africa (White et al., 2016). Drought
5 reduced growth and increased mortality of the holm oak, Quercus ilex, in the Iberian Peninsula of Spain,
6 Natalini et al. (2016). Portuguese shrublands experienced losses of many deciduous and evergreen oak
7 species, and increasing dominance of to pyrophytic xeric trees (Acácio et al., 2017). The 2012–2015 drought
8 in California caused: high canopy foliage die-back of the Giant Sequoia (Sequoiadendron giganteum)
9 (Stephenson et al., 2019), increased the dominance of oaks relative to pines resulting from increased water
10 deficit, and large-scale mortality from drought and insect pest outbreak interactions (McIntyre et al., 2015;
11 Fettig et al., 2019).
12
13 Species distribution or community composition changes have contributed to declines in diversity and/or
14 shifts towards more drought- or heat-adapted species (medium evidence, high agreement). Two conifer
15 species (Pinus longaeva, P. flexilis) shifted upslope 19 m from 1950 to 2016 in the Great Basin, USA,
16 (Smithers et al., 2018). Reduced winter precipitation caused native annual forbs to recede resulting in long-
17 lasting and potentially unidirectional reductions in diversity in a Californian grassland (Harrison et al.,
18 2018). More frequent extreme hot and dry weather between 1966 and 2010 caused declines in diversity
19 during the post-fire regeneration phase in the Fynbos of South Africa (Slingsby et al., 2017) resulting in
20 shifts towards species with higher temperature preferences (Slingsby et al., 2017). In Italy, Del Vecchio et al.
21 (2015) observed increases in plant cover and thermophilic species in coastal foredune habitats between 1989
22 and 2012.
23
24 In southern California, USA, areas of forest and woody shrublands are shifting to grasslands, driven by a
25 combination of climate and land use factors such as increased drought, fire ignition frequency and increases
26 in nitrogen deposition (robust evidence, high agreement) (Jacobsen and Pratt, 2018; Park et al., 2018; Park
27 and Jenerette, 2019; Syphard et al., 2019b).
28
29 The effects of climate change on heat, fuel, and wildfire ignition limitations show spatial and temporal
30 variation globally (see Section 2.3.6.1), but there have been a number of observed impacts in MTEs (medium
31 evidence, high agreement). Climate change has caused increases in fuel aridity and area burned by wildfire
32 across the western United States from 1985 to 2015 (Abatzoglou and Williams, 2016). Local and global
33 climatic variability led to a 4 year decrease in the average fire return time in Fynbos, South Africa when
34 comparing fires recorded between 1951 to 1975 and 1976 to 2000 (Wilson et al., 2010). For Chile, González
35 et al. (2018) reported a significant increase in the number, size, duration and simultaneity of large fires
36 during the 2010 to 2015 “megadrought” when compared to the 1990 to 2009 baseline.
37
38 2.4.3.5 Observed Changes in Savanna and Grasslands
39
40 Savannas consist of coexisting trees and grasses in the tropics and temperate regions (Archibald et al., 2019).
41 The global trend of woody encroachment reported in AR5 (Settele et al., 2014) is continuing (robust
42 evidence, high agreement, very high confidence) (see Table 2.S.1), with increases occurring in: temperate
43 savannas in North America (10-20% per decade) (Archer et al., 2017), tropical savannas in South America
44 (8% per decade), Africa ( 2.4% per decade) and Australia (1% per decade) (O'Connor et al., 2014; Espírito-
45 Santo et al., 2016; Skowno et al., 2017; Stevens et al., 2017; McNicol et al., 2018; Venter et al., 2018; Rosan
46 et al., 2019). Additionally, forest expansion into mesic savannas reported in AR5 (Settele et al., 2014), is
47 continuing in Africa, South America and Southeastern Asia (Marimon et al., 2014; Keenan et al., 2015;
48 Baccini et al., 2017; Ondei et al., 2017; Stevens et al., 2017; Aleman et al., 2018; Rosan et al., 2019).
49 Extreme high rainfall anomalies also contributed to an increase in herbaceous and foliar production in the
50 Sahel (Brandt et al., 2019; Zhang et al., 2019a).
51
52 New studies since AR5, using multiple study designs (experimental manipulations in lab and field, meta-
53 analyses and modelling), attribute climate change increases in woody cover to elevated atmospheric CO2
54 (Donohue et al., 2013; Nackley et al., 2018; Quirk et al., 2019) and increased rainfall amount and intensity
55 (robust evidence, high agreement) (Venter et al., 2018; Xu et al., 2018b; Zhang et al., 2019a). Direct
56 quantification of climate change drivers is confounded with local land use changes such as fire suppression
57 (Archibald, 2016; Venter et al., 2018), heavy grazing (du Toit and O’Connor, 2014; Archer et al., 2017),
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1 removal of native browsers, and specifically loss of mega-herbivores in Africa (medium evidence, medium
2 agreement) (Asner et al., 2016b; Daskin et al., 2016; Stevens et al., 2016; Davies et al., 2018). The relative
3 importance of the climate- and non-climate-related causes of woody plant vary between regions, but there is
4 general agreement that climate change impacts, specifically, increasing rainfall and rising CO2, are frequent
5 and strong contributing factors of woody cover increase (robust evidence, high agreement).
6
7 Extensive woody cover increases in non-forested biomes is reducing grazing potential (Smit and Prins,
8 2015), and changing the carbon stored per unit land area (González-Roglich et al., 2014; Puttock et al., 2014;
9 Pellegrini et al., 2016; Mureva et al., 2018) and hydrological characteristics (Honda and Durigan, 2016;
10 Schreiner-McGraw et al., 2020). Woody cover encroachment also reduces biodiversity by threatening fauna
11 and flora adapted to open ecosystems (Ratajczak et al., 2012; Smit and Prins, 2015; Pellegrini et al., 2016;
12 Andersen and Steidl, 2019).
13
14 The global extent of grasslands is declining significantly because of climate change (medium confidence). In
15 temperate and boreal zones, where about half of treelines are shifting, they are overwhelmingly expanding
16 poleward and upward, with accompanying loss of montane grassland (robust evidence, high agreement);
17 whereas tropical treelines have been generally stable (medium evidence, medium agreement) (Harsch et al.,
18 2009; Rehm & Feeley 2015; Silva et al., 2016; Andela et al., 2017; Song et al., 2018; Aide et al., 2019;
19 Gibson and Newman, 2019). The Eurasian steppes experienced a 1% increase in woody cover per decade
20 since 2000 (Liu et al., 2021) and Inner Mongolian grasslands in China experienced broad encroachment as
21 well (Chen et al., 2015). Climatic drivers of woody expansion in temperature limited grasslands, particularly
22 alpine grasslands, are most frequently attributed to warming (robust evidence, high agreement, high
23 confidence) (D'Odorico et al., 2012; Hagedorn et al., 2014), increases in water and nutrient availability from
24 thawing permafrost (medium evidence, high agreement) (Zhou et al., 2015b; Silva et al., 2016) and rising
25 CO2 (medium evidence, medium agreement) (Frank et al., 2015; Aide et al., 2019). Interactions between land
26 use changes: land abandonment, grazing management shifts, and fire suppression, and climate change are
27 contributing factors (Liu et al., 2021)
28
29 Remote sensing shows overall increasing trends in both the annual maximum NDVI and annual mean NDVI
30 in global grasslands ecosystems between 1982 and 2011 (Gao et al., 2016). Multiple lines of evidence
31 indicate that changes in grassland productivity are positively correlated with increases in mean annual
32 precipitation (Hoover et al., 2014; Brookshire and Weaver, 2015; Gang et al., 2015; Gao et al., 2016; Wilcox
33 et al., 2017; Wan et al., 2018). Increasing temperatures positively impact grassland production and biomass,
34 especially in temperature limited regions (Piao et al., 2014; Gao et al., 2016). However, grasslands in hot
35 areas are expected to decrease production with increases in temperature (limited evidence, low agreement)
36 (Gang et al., 2015). Nevertheless, grassland responses to warming and drought are being ameliorated by
37 increasing CO2 and associated improved water use efficiency (Roy et al., 2016). For example, in a cool
38 temperate grassland experiment, warming led to a longer growing season and elevated CO2 further extended
39 growing by conserving water, which enabled most species to remain active longer (medium evidence,
40 medium agreement) (Reyes-Fox et al., 2014).
41
42 2.4.3.6 Observed Changes in Tropical Forest
43
44 Overall declines of tropical forest cover (Kohl et al., 2015; Liu et al., 2015; Baccini et al., 2017; Harris et al.,
45 2021), with declines more than triple the gains (Harris et al., 2021) have been driven primarily by
46 deforestation and land conversion (robust evidence, high agreement) (Lewis et al., 2015; Curtis et al., 2018;
47 Espaciais, 2021). In opposition to this general trend, expansion of tropical forest cover into savannas and
48 grasslands has occurred in Africa, South America, and Australia (Baccini et al., 2017; Aleman et al., 2018;
49 Staver, 2018) (Marimon et al., 2014; Ondei et al., 2017; Stevens et al., 2017; Rosan et al., 2019).
50
51 Specific examples of climate-change driven range shifts of tropical deciduous forests upslope into alpine
52 grasslands have been documented in the Americas (Chacón-Moreno et al., 2021; Jiménez-García et al.,
53 2021) and in Asia (Sigdel et al., 2018). However, treeline behaviours are diverse. A study in Nepal recorded
54 that treeline fomed by Abies spectabilis had been stable for more than a century, while the upper limit of
55 large shrubs (Rhododendron campanulatum) had been advancing (Mainali et al., 2020). In both the Andes
56 (Harsch et al., 2009) and Himalaya (Singh et al., 2021) most treelines have been stable, leading (Rehm &
57 Feeley 2015) to postulate a "grass ceiling" that has been difficult for trees to penetrate. The treeline shifts
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1 that have occurred are probably driven by interactions between changing land-use, such as fire suppression,
2 and climate changes such as increased rainfall, warming and elevated CO2 either through CO2 fertilisation or
3 increases in water-use efficiency (medium evidence, medium agreement) (Cernusak et al., 2013; Huang et al.,
4 2013; Van Der Sleen et al., 2015; Yang et al., 2016).
5
6 Increases in productivity of tropical forests (Gatti et al., 2014; Brienen et al., 2015; Baccini et al., 2017),
7 Africa and SE Asia (Qie et al., 2017) have been attributed to elevated CO2 (robust evidence, medium
8 agreement) (Ballantyne et al., 2012; Brienen et al., 2015; Sitch et al., 2015; Yang et al., 2016; Mitchard,
9 2018). The rates of these increases have been slowing down in the central Amazon (Brienen et al., 2015; de
10 Meira Junior et al., 2020) and SE Asia (Qie et al., 2017). In contrast, the carbon sink (and hence rate of
11 biomass gain) in intact African forests was stable until 2010 and has only recently started to decline,
12 indicating asynchronous carbon sink saturation in Amazonia and Africa, the difference driven by rates of
13 tree mortality (Hubau et al. 2020). At a global level (Hubau et al 2020) argue that the carbon sink associated
14 with intact tropical forests peaked in the 1990s and is now in decline.
15
16 Declines in productivity are most strongly associated with warming (Sullivan et al., 2020), reduced growth
17 rates during droughts (Bennett et al., 2015; Bonai et al., 2016; Corlett, 2016), drought related mortality
18 (Brando et al., 2014; Zhou et al., 2014; Brienen et al., 2015; Corlett, 2016; McDowell et al., 2018), fire (Liu
19 et al., 2017), and cloud-induced radiation-limitation (robust evidence, high agreement) (Deb Burman et al.,
20 2020). Increases in frequency and severity of droughts and shorter tree residence times due to increases in
21 growth rates caused by elevated CO2 may be additional interactive factors increasing tree mortality (Malhi et
22 al., 2014; Brienen et al., 2015). Vulnerability to drought varies between tree species and sizes with large,
23 long-lived trees at highest risk of mortality (McDowell et al., 2018; Meakem et al., 2018). Mortality risk also
24 varies between forest types with seasonal rainforests appearing most vulnerable to drought (Corlett, 2016).
25
26 Lianas (long-stemmed woody vines) generally negatively impact trees, significantly reducing the growth of
27 heavily infested trees (Reis et al., 2020). They would benefit from climate change and disturbance (LingZi et
28 al., 2014; Hodgkins et al., 2018). The extent of their suitable niche can increase (Taylor and Kumar, 2016),
29 thereby decreasing forest biomass accumulation (robust evidence, high agreement) (van der Heijden et al.,
30 2013; Fauset et al., 2015; Estrada-Villegas et al., 2020).
31
32 Climate change continues to degrade forests by reducing resilience to pests and diseases, increasing species
33 invasion, facilitating pathogen spread (Malhi et al., 2014; Deb et al., 2018) and intensifying fire risk and
34 potential die-back (Lapola et al., 2018; Marengo et al., 2018). Drought, temperature increases and forest
35 fragmentation interact to increase the prevalence of fires in tropical forests (robust evidence, high
36 agreement). Warming increases water stress in trees (Corlett, 2016) and together with forest fragmentation,
37 dramatically increases desiccation of forest canopies—resulting in deforestation that then leads to even
38 hotter and drier regional climates (Malhi et al., 2014; Lewis et al., 2015). Warming and drought increase
39 invasion of grasses into forest edges and increase fire risk (robust evidence, high agreement) (Brando et al.,
40 2014; Balch et al., 2015; Lewis et al., 2015). Droughts and fires additively increase mortality and,
41 consequently, reduce canopy cover and aboveground biomass ( Cross-Chapter Paper 7; Brando et al., 2014,
42 2020; Balch et al., 2015; Lewis et al., 2015).
43
44 2.4.3.7 Observed Changes in Boreal and Temperate Forests
45
46 The IPCC Fifth Assessment Report found increased tree mortality, wildfire and plant phenology changes in
47 boreal and temperate forests (Settele et al., 2014). Expanding on those conclusions, this Assessment, using
48 analyses of causal factors, attributes to anthropogenic climate change the following observed changes in
49 boreal and temperate forests in the 20th and 21st centuries: upslope and poleward biome shifts at sites in Asia,
50 Europe, and North America (Section 2.4.3.2.1); range shifts of plants (Section 2.4.2.1); earlier blooming and
51 leafing of plants (Section 2.4.2.4); poleward shifts in tree-feeding insects (Section 2.4.2.1); increases in
52 insect pest outbreaks (Section 2.4.4.3.3); increases in area burned by wildfire in western North America
53 (Section 2.4.4.2.1); increased drought-induced tree mortality in western North America (Section 2.4.4.3.1);
54 and thawing of permafrost that underlies extensive areas of boreal forest (IPCC Sixth Assessment Report,
55 Working Group I, Chapter 2, Section 2.4.3.9). Atmospheric CO2 from anthropogenic sources has also
56 increased net primary productivity (Section 2.4.4.5.1). In summary, anthropogenic climate change has
57 caused substantial changes to temperate and boreal forest ecosystems, including biome shifts and increases
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1 in wildfire, insect pest outbreaks, and tree mortality, at a global mean surface temperature increase of 0.9º C
2 above the pre-industrial period (robust evidence, high agreement).
3
4 Other changes detected in boreal forests and consistent with, but not formally attributed to climate change,
5 include increased wildfire in Siberia (Section 2.4.4.2.3), long-lasting smoldering belowground fires in
6 Canada and the United States (Scholten et al., 2021), tree mortality in Europe (Section 2.4.4.3.3), and post-
7 fire shifts of boreal conifer to deciduous broadleaf tree species in Alaska (Mack et al., 2021). From 1930 to
8 1960, boreal forest growth became limited more by precipitation than temperature in the Northern
9 Hemisphere (Babst et al., 2019).
10
11 For some vegetation changes, land use and land management changes have exerted more influence than
12 climate change. These include upslope and poleward forest shifts in Europe following abandonment of
13 timber harvesting or livestock grazing (Section 2.4.3.2.2), changes in wildfire in Europe affected by fire
14 suppression, fire prevention, and agricultural abandonment (Section 2.4.4.2.3), and forest species
15 composition changes in Scotland due to nitrogen deposition from air pollution (Hester et al., 2019). Remote
16 sensing suggests that the area of temperate and boreal forests increased in Asia and Europe between 1982
17 and 2016 (Song et al., 2018) and in Canada between 1984 and 2015 (Guindon et al., 2018), but forest
18 plantations and regrowth are probable drivers (Song et al., 2018).
19
20 2.4.3.8 Observed Changes in Peatlands
21
22 Globally, peatland ecosystems store approximately 25% (600±100 GtC) of the world’s soil organic carbon
23 (Yu et al., 2010; Page et al., 2011; Hugelius et al., 2020) and 10% of the world’s freshwater resources
24 (Joosten and Clarke, 2002), despite only occupying 3% of the global land area (Xu et al., 2018a). The long-
25 term role of northern peatlands in the carbon cycle was mentioned for the first time in IPCC AR4 (IPCC,
26 2007b), while SR1.5 briefly mentioned the combined effects of climate and land-use change on peatlands
27 (IPCC, 2018b). New evidence confirms that climate change, including extreme weather events (e.g.,
28 droughts; Section 8.3.1.6), permafrost degradation (Section 2.3.2.5), sea-level rise (Section 2.3.3.3), and fire
29 (Section 5.4.3.2) (Henman and Poulter, 2008; Kirwan and Mudd, 2012; Turetsky et al., 2015; Page and
30 Hooijer, 2016; Swindles et al., 2019; Hoyt et al., 2020; Hugelius et al., 2020; Jovani‐Sancho et al., 2021;
31 Veraverbeke et al., 2021), superimposed on anthropogenic disturbances (for example, draining for
32 agriculture or mining; Section 5.2.1.1), has led to rapid losses of peatland carbon across the world (robust
33 evidence, high agreement) (Page et al., 2011; Leifeld et al., 2019; Hoyt et al., 2020; Turetsky et al., 2020;
34 Loisel et al., 2021). Other essential peatland ecosystem services, such as water storage and biodiversity, are
35 also being lost worldwide (robust evidence, high agreement) (Bonn et al., 2014; Martin-Ortega et al., 2014;
36 Tiemeyer et al., 2017).
37
38 The switch from carbon sink to source in peatlands globally is mainly attributable to changes in water table
39 depth, regardless of management or status (robust evidence, high agreement) (Lafleur et al., 2005; Dommain
40 et al., 2011; Lund et al., 2012; Cobb et al., 2017; Evans et al., 2021; Novita et al., 2021). Across the
41 temperate and tropical biomes, extensive drainage and deforestation have caused widespread water table
42 drawdowns and/or peat subsidence, as well as large CO2 emissions (medium evidence, high agreement).
43 Climate change is compounding these impacts (medium evidence, medium agreement). For example, in
44 Indonesia, the highest emissions from drained tropical peatlands were reported in the extremely dry year of
45 the 1997 El Niño (810-2570 TgC yr-1) (Page et al., 2002) and the 2015 fire season (380 TgC yr-1) (Field et
46 al., 2016). These prolonged dry seasons have also led to tree die-offs and fires, which are relatively new
47 phenomena in these latitudes (medium evidence, high agreement) (Cole et al., 2015; Mezbahuddin et al.,
48 2015; Fanin and van der Werf, 2017; Taufik et al., 2017; Cole et al., 2019). Low soil moisture contributes to
49 increased fire propagation (see Cross-Chapter Box 5, and Section 12.4.2.2; Dadap et al., 2019), causing
50 long-lasting fires responsible for smoke and haze pollution (robust evidence, high agreement) (Ballhorn et
51 al., 2009; Page et al., 2009; Gaveau et al., 2014; Huijnen et al., 2016; Page and Hooijer, 2016; Hu et al.,
52 2018; Vadrevu et al., 2019; Niwa et al., 2021). Increases in fires and smoke lead to habitat loss and
53 negatively impact regional faunal populations (limited evidence, high agreement) (Neoh et al., 2015; Erb et
54 al., 2018b; Thornton et al., 2018).
55
56 In large lowland tropical peatland basins that are less impacted by anthropogenic activities (i.e., Amazon and
57 Congo river basins), the direct impact of climate change is that of a decreased carbon sink (limited evidence,
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1 medium agreement) (Roucoux et al., 2013; Gallego-Sala et al., 2018; Wang et al., 2018a; Dargie et al., 2019;
2 Ribeiro et al., 2021). As for the temperate and boreal regions, climatic drying also tends to promote peat
3 oxidation and carbon loss to the atmosphere (medium evidence, medium agreement) (section 2.3.1.3.4)
4 (Helbig et al., 2020; Zhang et al., 2020). In Europe, increasing mean annual temperatures in the Baltic,
5 Scandinavia, and Continental Europe (Section 12.4.5.1) have led to widespread lowering of peatland water
6 tables at intact sites (Swindles et al., 2019), Sphagnum moss desiccation and die off (Bragazza, 2008; Lees et
7 al., 2019), and increased fire intensity and frequency resulting in rapid carbon loss (Davies et al., 2013;
8 Veraverbeke et al., 2021). Nevertheless, longer growing seasons and warmer, wetter climates have increased
9 carbon accumulation and promoted thick deposits regionally, as reported for some North American sites
10 (limited evidence, medium agreement) (Cai and Yu, 2011; Shiller et al., 2014; Ott and Chimner, 2016).
11
12 In high-latitude peatlands, the net effect of climate change on the permafrost peatland carbon sink capacity
13 remains uncertain (Abbott et al., 2016; McGuire et al., 2018b; Laamrani et al., 2020; Loisel et al., 2021; Sim
14 et al., 2021; Väliranta et al., 2021). Increasing air temperatures have been linked to permafrost degradation
15 and altered hydrological regimes (Section 2.3.3.2, Figure 2.4a, 2.4.3.9, and Box 5.1), which have led to rapid
16 changes in plant communities and biogeochemical cycling (robust evidence, high agreement) (Liljedahl et
17 al., 2016; Swindles et al., 2016; Voigt et al., 2017; Zhang et al., 2017b; Voigt et al., 2020; Sim et al., 2021).
18 In many instances, permafrost degradation triggers thermokarst land subsidence associated with local
19 wetting (robust evidence, high agreement) (Jones et al., 2013; Borge et al., 2017; Olvmo et al., 2020;
20 Olefeldt et al., 2021). Permafrost thaw in peatland-rich landscapes can also cause local drying through
21 increased hydrological connectivity and runoff (Connon et al., 2014). In the first decades following thaw,
22 increases in methane, CO2, and nitrous oxide emissions have been recorded from peatland sites, depending
23 on surface moisture conditions (Schuur et al., 2009; O’Donnell et al., 2012; Elberling et al., 2013; Matveev
24 et al., 2016; Euskirchen et al., 2020; Hugelius et al., 2020). Conversely, some evidence suggests increased
25 peat accumulation after thaw (Jones et al., 2013; Estop-Aragonés et al., 2018; Väliranta et al., 2021). There
26 is also a need to consider the impact of wildfire on permafrost thaw, due to its effect on soil temperature
27 regime (Gibson et al., 2018), wildfire as a}, as fire intensity and frequency have increased across the boreal
28 and Arctic biomes (limited evidence, high agreement) (Kasischke et al., 2010; Scholten et al., 2021).
29
30 Unfortunately, the CO2 emissions from degrading peatlands is contributing to climate change in a positive
31 feedback loop (robust evidence, high agreement). In the midlatitudes, widespread anthropogenic disturbance
32 led to large historical GHG emissions and current legacy emissions of 0.15 PgC yr-1 between 1990 and 2000
33 (limited evidence, high agreement) (Maljanen et al., 2010; Tiemeyer et al., 2016; Drexler et al., 2018; Qiu et
34 al., 2021). About 80 million ha of peatlands have been converted to agriculture, equivalent to 72 PgC
35 emissions between 850–2010 CE (Leifeld et al., 2019; Qiu et al., 2021). In southeast Asia, an estimated 20–
36 25 Mha of peatlands have been converted to agriculture with carbon currently being lost at a rate of
37 ~155 ± 30 MtC yr−1 (Miettinen et al., 2016; Leifeld et al., 2019; Hoyt et al., 2020). Extensive deforestation
38 and drainage have caused widespread peat subsidence and large CO2 emissions at a current average of ~10 ±
39 2 t ha-1 yr-1 (excluding fires, (Hoyt et al., 2020)), with values estimated from point subsidence measurements
40 being as high as 30–90 t CO2 ha−1 yr−1 locally (robust evidence, high agreement) (Wösten et al., 1997;
41 Matysek et al., 2018; Swails et al., 2018; Evans et al., 2019; Conchedda and Tubiello, 2020; Anshari et al.,
42 2021). On balance, at the global scale, increases in GHG emissions from peatlands have primarily come
43 from the compounded effects of land-use change, drought, and fire, with emissions from some thawing
44 permafrost peatlands (robust evidence, high agreement).
45
46 2.4.3.9 Observed Changes in Polar Tundra
47
48 Warming at high latitudes, documented in both AR4 and AR5, is leading to earlier snow and sea ice melt and
49 longer growing seasons (WGI AR6) which are continuing to alter tundra plant communities (medium
50 evidence, high agreement) (Post et al., 2009; Gauthier et al., 2013). Woody encroachment and increases in
51 vegetation productivity observed in both AR4 and AR5 are widespread and continuing. Both experiments
52 and monitoring indicate that climate warming is causing increases in shrub, grass and sedge abundance,
53 density, frequency and height, with decreases in mosses and/or lichens (robust evidence, high agreement)
54 (Myers-Smith et al., 2011; Bjorkman et al., 2018; Bjorkman et al., 2019). Shrub growth is climate-sensitive
55 and greater in years with warmer growing seasons (Myers-Smith et al., 2015). Plant species that prefer
56 warmer conditions are increasing (Elmendorf et al., 2015; Bjorkman et al., 2018), plant cover is increasing
57 and bare ground is decreasing in long-term monitoring plots (Bjorkman et al., 2019; Myers-Smith et al.,
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1 2019). Animals such as moose, beavers and songbirds may already be responding to these vegetation
2 changes by expanding their ranges northward or upslope into shrub tundra (Boelman et al., 2015; Tape et al.,
3 2016a; Tape et al., 2016b; Tape et al., 2018).
4
5 In addition to direct warming, indirect effects of climate change such as thawed permafrost, altered
6 hydrology and enhanced nutrient cycling (as observed in AR4 and AR5) continue and are causing
7 pronounced vegetation changes (medium evidence, medium agreement) (Schuur et al., 2009; Natali et al.,
8 2012). Soil moisture status influences temperature sensitivity of plant growth and canopy heights (Myers-
9 Smith et al., 2015; Ackerman et al., 2017; Bjorkman et al., 2018). In tundra ecosystems permafrost thawing
10 can decouple below-ground plant growth dynamics from above-ground dynamics, with below-ground root
11 growth continuing until soils refreeze in autumn ( Cross-Chapter Paper 6; Iversen et al., 2015; Blume-Werry
12 et al., 2016; Radville et al., 2016).
13
14 2.4.4 Observed Changes in Ecosystem Processes and Services
15
16 2.4.4.1 Observed Browning of Rivers and Lakes
17
18 In boreal coniferous areas there has been an increase in terrestrial derived dissolved organic carbon (DOC)
19 transport into rivers and lakes, which has caused increased opacity and shift toward a brown colour
20 (browning). This process was not given much attention in AR5 even though it is a consequence of climate
21 change: hydrological intensification, greening of the Northern Hemisphere, and degradation of carbon sinks
22 in peatlands (robust evidence, high agreement) (Solomon et al., 2015; Catalán et al., 2016; Crowther et al.,
23 2016; de Wit et al., 2016; Finstad et al., 2016; Creed et al., 2018; Hayden et al., 2019) factors that enhance
24 terrestrial productivity, alter vegetation communities, and affect the hydrological control on production and
25 transport of DOC (Weyhenmeyer et al., 2016). Non climate-related drivers of browning are: declining
26 atmospheric sulphur deposition, forestry practices and land-use changes (see Table 2.S.1 for detail).
27
28 Browning creates a positive feedback by absorbing photosynthetically active radiation accelerating upper
29 water (epilimnetic) warming (Solomon et al., 2015). Browning of lakes leads to shallower and more stable
30 thermoclines and thus, overall deep water cooling (Solomon et al., 2015; Williamson et al., 2015) and can
31 provoke a transition of the seasonal mixing regime from a mixed lake (polymictic) to one that is seasonally
32 stratified (Kirillin and Shatwell, 2016).
33
34 The ecological responses of browning need to be considered as concomitant effects of climate change and
35 nutrient status. Results from long-term, large-scale lake experiments were variable, showing both strong
36 synergistic effects (Urrutia-Cordero et al., 2016) and no significant effects of browning on plankton
37 community food webs (Rasconi et al., 2015). Browning has driven a shift from auto- to
38 heterotrophic/mixotrophic-based production (Wilken et al., 2013; Urrutia-Cordero et al., 2017) and supports
39 heterotrophic metabolism of the bacterial community (Zwart et al., 2016). Browning may also accelerate
40 primary production through input of nutrients associated with DOM in nutrient poor lakes and increase
41 cyanobacteria, which better cope with low light intensities (Huisman et al., 2018) and toxin levels (Urrutia-
42 Cordero et al., 2016). However, the synergistic impacts of browning and climate change on aquatic
43 communities depends on regional precipitation patterns (Weyhenmeyer et al., 2016), watershed type (de Wit
44 et al., 2016), and food chain length (Hansson et al., 2013). Quantitative attribution of browning to climate
45 change remains difficult (medium evidence, medium confidence).
46
47 In summary, new studies since AR5 have explicitly estimated the effects of warming and browning on
48 freshwaters in boreal areas with complex positive and negative repercussions on water temperature profiles
49 (lower vs upper water) (high confidence) and primary production (medium confidence).
50
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1
2 Figure 2.5: Large scale observed changes in freshwater ecosystems attributed to climate change over more than
3 four decades. For description and references, see 2.3.3, 2.4.2, and 2.5.3.6.2.
4
5
6 2.4.4.2 Observed Changes in Wildfire
7
8 2.4.4.2.1 Detection and attribution of observed changes in wildfire
9 Wildfire is a natural and essential component of many forest and other terrestrial ecosystems. Excessive
10 wildfire, however, can kill people, cause respiratory disease, destroy houses, emit carbon dioxide, and
11 damage ecosystem integrity (see Section 2.4.4.2, 2.4.4.4). Anthropogenic climate change increases wildfire
12 by exacerbating its three principal driving factors—heat, fuel, and ignition (Moritz et al., 2012; Jolly et al.,
13 2015). Non-climatic factors also contribute to wildfires—in tropical areas fires are set intentionally to clear
14 forest for agricultural fields and livestock pastures (Bowman et al., 2020b). Urban areas and roads create
15 ignition hazards. Governments in many temperate zone countries implement policies to suppress fires, even
16 natural ones, producing unnatural accumulations of fuel in the form of coarse woody debris and high
17 densities of small trees (Ruffault and Mouillot, 2015; Hessburg et al., 2016; Andela et al., 2017; Balch et al.,
18 2017; Lasslop and Kloster, 2017; Aragao et al., 2018, (Kelley et al., 2019). Globally, 4.2 million km2 of land
19 per year burned on average from 2002 to 2016 (Giglio et al., 2018) with the highest fire frequencies in the
20 Amazon rainforest, deciduous forests and savannas in Africa, and deciduous forests in northern Australia
21 (Earl and Simmonds, 2018; Andela et al., 2019).
22
23 Since the IPCC Fifth Assessment Report and the IPCC Special Report on Land, published research has
24 detected increases in the area burned by wildfire, analysed relative contributions of climate and non-climate
25 factors, and attributed burned area increases above natural levels to anthropogenic climate change in one part
26 of the world – western North America (robust evidence, high agreement) (Abatzoglou and Williams, 2016;
27 Partain et al., 2016; Kirchmeier‐Young et al., 2019; Mansuy et al., 2019; Bowman et al., 2020b). Across the
28 western United States, increases in vegetation aridity due to higher temperatures from anthropogenic climate
29 change doubled burned area from 1984 to 2015 over what would have burned due to non-climate factors,
30 including unnatural fuel accumulation from fire suppression, with the burned area attributed to climate
31 change accounting for 49% (32-76%, 95% confidence interval) of cumulative burned area (Abatzoglou and
32 Williams, 2016). Anthropogenic climate change has doubled the severity of a southwest North American
33 drought from 2000 to 2020 that has reduced soil moisture to its lowest levels since the 1500s (Williams et
34 al., 2020), driving half of the increase in burned area (Abatzoglou and Williams, 2016; Holden et al., 2018;
35 Williams et al., 2019). In British Columbia, Canada, the increased maximum temperatures due to
36 anthropogenic climate change increased burned area in 2017 to its highest extent in the 1950-2017 record,
37 seven to eleven times the area that would have burned without climate change (Kirchmeier-Young et al.,
38 2019). In Alaska, USA, the high maximum temperatures and extremely low relative humidity due to
39 anthropogenic climate change accounted for 33‒60% of the probability of wildfire in 2015, when the area
40 burned was the second highest in the 1940-2015 record (Partain et al., 2016). In protected areas of Canada
41 and the United States, climate factors (temperature, precipitation, relative humidity, evapotranspiration)
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1 accounted for 60% of burned area from local human and natural ignitions from 1984 to 2014, outweighing
2 local human factors (population density, roads, and built area) (Mansuy et al., 2019).
3
4 In summary, field evidence shows that anthropogenic climate change has increased the area burned by
5 wildfire above natural levels across western North America in the period 1984-2017, at global mean surface
6 temperature increases of 0.6ºC -0.9ºC, increasing burned area up to 11 times in one extreme year and
7 doubling burned area over natural levels in a 32 year period (high confidence).
8
9 2.4.4.2.2 Observed changes in wildfire globally
10 For global terrestrial area as a whole, wildfire trends vary depending on the time period of analysis. From
11 1900 to 2000, global average fire frequency, based on field data, increased 0.4% but the change was not
12 statistically significant, (Mouillot and Field, 2005; Gonzalez et al., 2010b). Fire frequency increased on one-
13 third of global land, mainly from burning for agricultural clearing in Africa, Asia, and South America,
14 slightly less than the area of fire frequency decrease, mainly from fire suppression across Australia, North
15 America, and Russia (Gonzalez et al., 2010b). Analyses of the Global Fire Emissions Database document
16 that from 1996 to 2015, global burned area decreased at a rate of –0.7% y-1 (Forkel et al., 2019) but the
17 change was not statistically significant, (Giglio et al., 2013). From 1998 to 2015, global burned area
18 decreased at a rate of −1.4 ± 0.5% y-1 (Giglio et al., 2013; Andela et al., 2017). The area of fire increases was
19 a third of the area of decreases, due to reduction of vegetation cover from agricultural expansion and
20 intensification (Andela et al., 2017) and increased precipitation (Forkel et al., 2019). Furthermore, much of
21 the decreasing trend derives from two years: 1998 with high burned area and 2013 with low burned area
22 (Forkel et al., 2019). Wildfire does not show a clear long-term trend for the world as a whole because of
23 increases and decreases in different regions (medium evidence, medium agreement).
24
25 Where global average burned area has decreased in the past two decades, higher correlations of rates of
26 change in burning to human population density, cropland area, and livestock density than to precipitation
27 indicate that agricultural expansion and intensification were main causes (Andela et al., 2017). The global
28 decrease of fire frequency from 2000 to 2010 is correlated to increasing human population density (Knorr et
29 al., 2014). The fire-reduction effect of reduced vegetation cover following expansion of agriculture and
30 livestock herding can counteract the fire-increasing effect of increased heat of climate change (Lasslop and
31 Kloster, 2017; Arora and Melton, 2018; Forkel et al., 2019). The reduction of burning needed after the initial
32 clearing for agricultural expansion drives much of the decline in fire in the tropics (Andela et al., 2017; Earl
33 and Simmonds, 2018; Forkel et al., 2019). The human influence on fire ignition can be seen through the
34 decrease documented on holy days (Sundays and Fridays), traditional religious days of rest (Earl et al.,
35 2015). Overall, human land use exerts an influence on wildfire trends for global terrestrial area as a whole
36 that can be stronger than climate change (medium confidence).
37
38 2.4.4.2.3 Observed changes in wildfire in individual regions
39 While burned area has increased in parts of Asia, Australia, Europe, and South America, published research
40 has not yet attributed the increases to anthropogenic climate change (medium evidence, high agreement).
41
42 In the Amazon, deforestation for agricultural expansion and the degradation of forests adjacent to deforested
43 areas cause wildfire in moist humid tropical forests not adapted to fire (robust evidence, high agreement)
44 (Fonseca et al., 2017; van Marle et al., 2017; da Silva et al., 2018; da Silva et al., 2021; dos Reis et al., 2021;
45 Libonati et al., 2021). Roads facilitate deforestation, fragmenting the rainforest and increasing the dryness
46 and flammability of vegetation (Alencar et al., 2015). Extreme droughts that occur during warm phases of
47 the El Niño-Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation combine with the
48 degradation of vegetation to cause extreme fire events (robust evidence, high agreement) (Fonseca et al.,
49 2017; Aragao et al., 2018; da Silva et al., 2018; Burton et al., 2020; dos Reis et al., 2021; Libonati et al.,
50 2021). In the State of Roraima, Brazil, distance to roads, infrastructure that enables deforestation, and ENSO
51 were the two factors most explaining fire occurrence in the extreme 2015-2016 fire season (Fonseca et al.,
52 2017). From 1973 to 2014, burned area increased in the Amazon, coinciding with increased deforestation
53 (van Marle et al., 2017). In the State of Acre, Brazil, burned area increased 36-fold from 1984 to 2016, with
54 43% burned area in agricultural and livestock settlement areas (da Silva et al., 2018). In 2019, the extreme
55 fire year 2019, 85% of the area burned in the Amazon occurred in areas deforested in 2018 (Cardil et al.,
56 2020). Even though relatively higher moisture in 2019 led to burning below the 2002-2019 average across
57 most of South America, burning in areas of recent deforestation in the Amazon were above the 2002-2019
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1 average, indicating that deforestation, not meteorological conditions, triggered the 2019 fires (Kelley et al.,
2 2021; Libonati et al., 2021). Furthermore, from 1981 to 2018, deforestation in the Amazon reduced moisture
3 inputs to the lower atmosphere, increasing drought and fire in a self-reinforcing feedback (Xu et al., 2020).
4 In the Amazon, deforestation exerts an influence on wildfire that can be stronger than climate change (robust
5 evidence, high agreement).
6
7 In Australia, burned area increased significantly between the periods 1950-2002 and 2003-2020 in the
8 southeast state of Victoria, with the area burned in the 2019-2020 bushfires the highest on record
9 (Lindenmayer and Taylor, 2020). In addition to the deaths of dozens of people and destruction of thousands
10 of houses, the 2019-2020 Australia bushfires burned almost half of the area protected for conservation in
11 Victoria and two-thirds of forests allocated for timber harvesting (Lindenmayer and Taylor, 2020), wildlife,
12 and extensive areas of habitat for threatened plant and animal species (Geary et al., 2021). Generally, past
13 timber harvesting did not lead to more severe fire canopy damage (Bowman et al., 2021). Across
14 Southeastern Australia, the fraction of vegetated area that burned increased significantly in eight of the 32
15 bioregions from 1975 to 2009 but decreased significantly in three bioregions (Bradstock et al., 2014).
16 Increases in four bioregions were correlated to increasing temperature and decreasing precipitation.
17 Decreases in burned area occurred despite increased temperature and decreased precipitation. Analyses of
18 climate across Australia from 1950 to 2017 (Dowdy, 2018; Harris and Lucas, 2019) and during periods with
19 extensive fires in 2017 in eastern Australia (Hope et al., 2019), in 2018 in Northeastern Australia (Lewis et
20 al., 2020), and in period 2019-2020 in Southeastern Australia (Abram et al., 2021; van Oldenborgh et al.,
21 2021) indicate that temperature and drought extremes due to the El Niño-Southern Oscillation, Southern
22 Annular Mode, and other natural interdecadal cycles drive interannual variability of fire weather. While the
23 effects of interdecadal climate cycles on fire are superimposed on long-term climate change, the relative
24 importance of anthropogenic climate change in explaining changes in burned area in Australia remains
25 unquantified (medium evidence, high agreement).
26
27 In Africa, the rate of change of burned area for the continent as a whole ranged from a non-statistically
28 significant –0.45% y-1 from 2002 to 2016 (Zubkova et al., 2019) to a significant –1.9% y-1 from 2001 to
29 2016 (Wei et al., 2020). Burned area decreases coincided with areas of agricultural expansion or areas where
30 drought reduced fuel loads (Zubkova et al., 2019; Wei et al., 2020). It is possible, however, that the 500 m
31 spatial resolution of Modis remote sensing fire data underestimates burned area in Africa by half by missing
32 small fires (Ramo et al., 2021). In the Serengeti-Mara savanna of east Africa, burned area showed no
33 significant change from 2001 to 2014, although an increase in domestic livestock would tend to reduce the
34 grass cover that fuels savanna fires (Probert et al., 2019).
35
36 In Mediterranean Europe, burned area for the region as a whole decreased from 1985 to 2011 (Turco et al.,
37 2016), although burned area for Spain did not show a significant long-term increase from 1968 to 2010
38 (Moreno et al., 2014) while burned area for Portugal in 2017 was the highest in the period 1980-2017
39 (Turco et al., 2019). Increased summer maximum temperature and decreased soil moisture explained most of
40 observed burned area, suggesting a contribution of climate change, but fire suppression, fire prevention,
41 agricultural abandonment, and reforestation, and reduction of forest area exerted even stronger influences on
42 burned area than climate across Mediterranean Europe (robust evidence, high agreement) (Moreno et al.,
43 2014; Turco et al., 2017; Viedma et al., 2018; Turco et al., 2019).
44
45 In the Arctic tundra and boreal forest, where wildfire has naturally been infrequent, burned area showed
46 statistically significant increases of ~50% y-1 across Siberia, Russia, from 1996 to 2015 (Ponomarev et al.,
47 2016) and 2% y-1 across Canada from 1959 to 2015 (Hanes et al., 2019). Wildfire burned ~ 6% of the area of
48 four extensive Arctic permafrost regions in Alaska, USA, eastern Canada, and Siberia from 1999 to 2014
49 (Nitze et al., 2018). In boreal forest in the Northwest Territories, Canada, and Alaska, USA, the area burned
50 by wildfire increased at a statistically significant rate of 6.8% y-1 in the period 1975-2015, (Veraverbeke et
51 al., 2017), with smouldering belowground fires that lasted through the winter covering ~1% of burned area
52 in the period 2002-2016 (Scholten et al., 2021). While burned area was correlated to temperature and
53 reduced precipitation in Siberia (Ponomarev et al., 2016; Masrur et al., 2018) and to lightning, correlated
54 with temperature and precipitation in the Northwest Territories and Alaska (Veraverbeke et al., 2017), no
55 attribution analyses have examined relative influences of climate and non-climate factors.
56
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1 In Indonesia, deforestation and draining of peat swamp forests dries out the peat, providing substantial fuel
2 for fires (Page and Hooijer, 2016). Extreme fire years in Indonesia, including 1997, 2006, and 2015, coincide
3 with extreme heat and aridity during the warm phase of the El Niño-Southern Oscillation (Field et al., 2016).
4 Fire-resistant forest in 2019 covered only 3% of peatlands and 4.5% of non-peatlands on Sumatra and
5 Kalimantan (Nikonovas et al., 2020).
6
7 In Chile, burned area in the summer of 2016–2017 was 14 times the mean for the period 1985–2016 and the
8 highest on record (Bowman et al., 2019). While that extreme fire year coincided with the highest daily mean
9 maximum temperature in the period 1979–2017 (Bowman et al., 2019), in central Chile, the area of highest
10 fire activity, burned area from 1976 to 2013 showed highest correlation to precipitation cycles of the El
11 Niño-Southern Oscillation and temperature cycles of the Antarctic Oscillation (Urrutia-Jalabert et al., 2018).
12
13 Overall, burned area has increased in the Amazon, the Arctic, Australia, and parts of Africa and Asia,
14 consistent with, but not formally attributed to anthropogenic climate change (medium evidence, high
15 agreement). Deforestation, peat draining, agricultural expansion or abandonment, fire suppression, and
16 interdecadal cycles such as the El Niño-Southern Oscillation exert a stronger influence than climate change
17 on wildfire trends in numerous regions outside of North America (high confidence).
18
19 2.4.4.2.4 Observed changes in fire seasons globally
20 IPCC AR6 Working Group 1, Chapter 12, has assessed fire weather, while this chapter assesses burned area
21 and fire frequency. The global increases in temperature of anthropogenic climate change have increased
22 aridity and drought, lengthening the fire weather season (annual period with a heat and aridity index greater
23 than half of its annual range) on one-quarter of global vegetated area and increasing average fire season
24 length by one-fifth, from 1979 to 2013 (Jolly et al., 2015). Climate change has contributed to increases in the
25 fire weather season or the probability of fire weather conditions in the Amazon (Jolly et al., 2015), Australia
26 (Dowdy, 2018; Abram et al., 2021; van Oldenborgh et al., 2021), Canada (Hanes et al., 2019), central Asia
27 (Jolly et al., 2015), East Africa (Jolly et al., 2015), and North America (Jain et al., 2017; Williams et al.,
28 2019; Goss et al., 2020). In forest areas, the burned area is correlated to fuel aridity, a function of
29 temperature; in non-forest areas, the burned area is correlated to high precipitation in the previous year,
30 which can produce high grass fuel loads (Abatzoglou et al., 2018). Fire use in agriculture and livestock
31 raising or other factors have generated a second fire season on approximately one-quarter of global land
32 where fire is present, despite sub-optimal fire weather in the second fire season (Benali et al., 2017). In
33 summary, anthropogenic climate change, through a 0.9ºC surface temperature increase since the pre-
34 industrial period, has lengthened or increased the frequency of periods with heat and aridity that favour
35 wildfire on up to one-quarter of vegetated area, since 1979 (robust evidence, high agreement).
36
37 2.4.4.2.5 Observed changes in post-fire vegetation
38 Globally, fire has contributed to biome shifts (Section 2.4.3.2) and tree mortality (Section 2.4.4.2, 2.4.4.3)
39 attributed to anthropogenic climate change. Research since the IPCC Fifth Assessment Report has also found
40 vegetation changes from wildfire due to climate change. Through increased temperature and aridity,
41 anthropogenic climate change has driven post-fire changes in plant regeneration and species composition in
42 South Africa (Slingsby et al., 2017) and tree regeneration in the western United States (Davis et al., 2019b).
43 In the Fynbos vegetation of the Cape Floristic Region, South Africa, post-fire heat and drought and legacy
44 effects of exotic plant species reduced native plant species regeneration, decreasing species richness 12%
45 from 1966 to 2010 and shifting the average temperature tolerance of species upward by 0.5ºC (Slingsby et
46 al., 2017). In burned areas across the western United States, the increasing heat and aridity of anthropogenic
47 climate change from 1979 to 2015 pushed low-elevation ponderosa pine (Pinus ponderosa) and Douglas-fir
48 (Pseudotsuga menziesii) forests across critical thresholds of heat and aridity that reduced post-fire tree
49 regeneration by half (Davis et al., 2019b). In the Southwestern United States of America, where
50 anthropogenic climate change has caused drought (Williams et al., 2019) and increased wildfire (Abatzoglou
51 and Williams, 2016), high-severity fires have converted some forest patches to shrublands (Barton and
52 Poulos, 2018). Field evidence shows that anthropogenic climate change and wildfire together have altered
53 vegetation species composition in the Southwestern USA and in the Cape floristic region, South Africa,
54 reducing post-fire natural regeneration and species richness of tree and other plant species, between 1966
55 and 2015, at global mean surface temperature increases of 0.3-0.9ºC (medium evidence, high agreement).
56
57
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1 [START FAQ2.3 HERE]
2
3 FAQ2.3: Is climate change increasing wildfire?
4
5 In the Amazon, Australia, North America, Siberia, and other regions, wildfires are burning wider areas than
6 in the past. Analyses show that human-caused climate change has driven the increases in burned area in the
7 forests of western North America. Elsewhere, deforestation, fire suppression, agricultural burning, and
8 short-term cycles like El Niño can exert a stronger influence than climate change. Many forests and
9 grasslands naturally require fire for ecosystem health but excessive wildfire can kill people, destroy homes,
10 and damage ecosystems.
11
12
13
14 Figure FAQ2.3.1: (a) Springs Fire, May 2, 2013, Thousand Oaks, California, USA (photo by Michael Robinson
15 Chávez, Los Angeles Times). (b) Cumulative area burned by wildfire in the western U.S., with (orange) and without
16 (yellow) the increased heat and aridity of climate change (Abatzoglou and Williams, 2016).
17
18 Wildfire is a natural and essential part of many forest, woodland, and grassland ecosystems, killing pests,
19 releasing plant seeds to sprout, thinning out small trees, and serving other functions essential for ecosystem
20 health. Excessive wildfire, however, can kill people, cause breathing illnesses from the smoke, destroy
21 homes (Figure FAQ2.1a), and damage ecosystems.
22
23 Human-caused climate change increases wildfire by intensifying its principal driving factor – heat. The heat
24 of climate change dries out vegetation and accelerates burning. Non-climate factors also cause wildfires.
25 Agricultural companies, small farmers, and livestock herders in many tropical areas cut down forests and
26 intentionally set fires to clear fields and pastures. Cities, towns, and roads increase the number of fires that
27 people ignite. Governments in many countries suppress fires, even natural ones, producing unnatural
28 accumulations of fuel in the form of coarse woody debris and dense stands of small trees. The fuel
29 accumulations cause particularly severe fires that burn into tree crowns.
30
31 Evidence shows that human-caused climate change has driven increases in the area burned by wildfire in the
32 forests of western North America. Across the western U.S., the higher temperatures of human-caused
33 climate change doubled burned area from 1984 to 2015, compared with what would have burned without
34 climate change (Figure FAQ2.1b). The additional area burned, 4.9 million hectares, is greater than the land
35 area of Switzerland. In this region, human-caused climate change has driven a drought from 2000 to 2020
36 that is the most severe since the 1500s, severely increasing the aridity of vegetation. In British Columbia,
37 Canada, the higher maximum temperatures of human-caused climate change increased burned area in 2017
38 to its widest extent in the 1950-2017 record, seven to eleven times the area that would have burned without
39 climate change. Moreover, in national parks and other protected areas of Canada and the U.S., climate
40 factors explained the majority of burned area from 1984 to 2014, with climate factors (temperature, rainfall,
41 aridity) outweighing local human factors (population density, roads, and urban area).
42
43 In other regions, wildfires are also burning wider areas and occurring more often. This is consistent with
44 climate change but analyses have not yet shown if climate change is more important than other factors. In the
45 Amazon, deforestation by companies, farmers, and herders who cut down and intentionally burn rainforests
46 to expand agricultural fields and pastures causes wildfires even in relatively moister years. Drought
47 exacerbates these fires. In Australia, much of the southeastern part of the continent has experienced extreme
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1 wildfire years, but analyses suggest that El Niño, a heat phenomenon that cycles up and down periodically, is
2 more important than long-term climate change. In Indonesia, intentional burning of rainforests for oil palm
3 plantations and El Niño seem to be more important than long-term climate change. In Mediterranean Europe,
4 fire suppression seems to have prevented any increasing trend in burned area but suppression and
5 abandonment of agricultural lands have allowed fuel to build up in some areas and contribute to major fires
6 in years of extreme heat. In Canada and Siberia, wildfires are now burning more often in permafrost areas
7 where fire had been rare, but analyses are lacking on the relative influence of climate change. For the world
8 as a whole, satellite data indicate that the vast amount of land that converted from forest to farmland from
9 1998 to 2015 actually decreased total burned area. Nevertheless, the evidence from the forests of western
10 North America shows that human-caused climate change has, on one continent, clearly driven increases in
11 wildfire.
12
13 [END FAQ2.3 HERE]-
14
15
16 2.4.4.3 Observed Changes in Tree Mortality
17
18 2.4.4.3.1 Observed tree mortality globally
19 Anthropogenic climate change can cause tree mortality directly through increased aridity or drought (Section
20 2.4.4.3.3) or indirectly through wildfire (Section 2.4.4.2.1) and insect pests (Section 2.4.4.3.3). Catastrophic
21 failure of the plant hydraulic system, in which a lack of water causes the xylem to lose hydraulic
22 conductance, is the principal mechanism of drought-induced tree death (Anderegg et al., 2016; Adams et al.,
23 2017; Anderegg et al., 2018; Choat et al., 2018; Menezes-Silva et al., 2019; Brodribb et al., 2020).
24
25 Up through the IPCC Fifth Assessment Report (Settele et al., 2014), detection and attribution analyses had
26 found that anthropogenic climate change, with global temperature increases of 0.3°-0.9ºC above the pre-
27 industrial period and increases in aridity exceeding the effects of local non-climate change factors, caused
28 three cases of drought-induced tree mortality of up to 20% in the period 1945-2007, in western North
29 America (van Mantgem et al., 2009), the African Sahel (Gonzalez et al., 2012), and North Africa (le Polain
30 de Waroux and Lambin, 2012). Increased wildfire and pest infestations, driven by climate change, also
31 contributed to the North American tree mortality (van Mantgem et al., 2009). In addition, a meta-analysis of
32 published cases found that drought consistent with, but not formally attributed, to climate change, had
33 caused tree mortality at 88 sites in boreal, temperate and tropical ecosystems (Allen et al., 2010), with 49
34 additional cases found by the IPCC Fifth Assessment report (Settele et al., 2014).
35
36 Since the IPCC Fifth Assessment Report (Settele et al., 2014), global meta-analyses have found at least 15
37 (Allen et al., 2015) and 25 (Hartmann et al., 2018) additional sites of drought-induced tree mortality around
38 the world. These and other global analyses found more rapid mortality than previously (Allen et al., 2015),
39 rising background mortality (Allen et al., 2015), mortality increasing with drought severity (Greenwood et
40 al., 2017), mortality of tropical trees increasing with temperature (Locosselli et al., 2020), mortality
41 increasing with tree size for many species (Bennett et al., 2015), mortality predominantly at the dry edge of
42 species ranges (Anderegg et al., 2019a), and three-fourths of drought-induced mortality cases leading to a
43 change in the dominant species (Batllori et al., 2020). Multiple non-climate factors contribute to tree
44 mortality, including timber cutting, livestock grazing, and air pollution (Martinez-Vilalta and Lloret, 2016).
45 Globally, tropical dry forests lost, from all causes, 95,000 km2, 8% of their total area, from 1982 to 2016, the
46 most extensive area of mortality of any biome (Song et al., 2018).
47
48 In summary, anthropogenic climate change has caused drought-induced tree mortality up to 20% in the
49 period 1945-2007 in western North America, the African Sahel, and North Africa, through global
50 temperature increases of 0.3°-0.9ºC above the pre-industrial period and increases in aridity, and contributed
51 to over 100 other cases of drought-induced tree mortality in Africa, Asia, Australia, Europe, and North and
52 South America (high confidence). Field observations document accelerating mortality rates, rising
53 background mortality, and post-mortality vegetation shifts (high confidence). Water stress, leading to plant
54 hydraulic failure, is the principal mechanism of drought-induced tree mortality. Timber cutting, agricultural
55 expansion, air pollution, and other non-climate factors also contribute to tree death.
56
57
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1 2.4.4.3.2 Observed tree mortality in tropical ecosystems
2 In the Brazilian Amazon, deforestation to clear agricultural land comprises the principal cause of tree
3 mortality, reducing forest cover an average of 13,900 km2 y-1 from 1988 to 2020 (Espaciais, 2021). In
4 addition, an annual average temperature increase of 1.2ºC from 1950 to 2018 (Marengo et al., 2018)
5 contributed to mortality in a set of 310 Amazon field plots of ~40% from 1983 to 2011 (Brienen et al.,
6 2015). In another set of plots, mortality among newly recruited trees of mesic genera increased and drought-
7 tolerant genera became more abundant from 1985 to 2015 (Esquivel-Muelbert et al., 2019). In other plots,
8 tree mortality did not show a statistically significant change from 1965 to 2016 but rose abruptly in severe
9 drought years, mainly during warm phases of the El Niño-Southern Oscillation (ENSO) (Aleixo et al., 2019).
10 Nearly half the area of the Amazon has experienced extremely dry conditions during ENSO warm phases,
11 which can cause extensive wildfire (Section 2.4.4.2.3). Wildfire can increase tree mortality rates by >600%
12 above rates in non-burned areas, with the higher mortality persisting up to a decade after a fire (Silva et al.,
13 2018; Berenguer et al., 2021). Climate change has contributed to tree mortality in Amazon rainforest
14 (medium evidence, medium agreement).
15
16 In the African Sahel field research has continued to detect tree mortality, ranging from 20% to 90% in the
17 period 1965-2018 (Kusserow, 2017; Trichon et al., 2018; Dendoncker et al., 2020), and declines in tree
18 biodiversity, with local losses of tree species up to 80% in the period 1970-2014 (Hanke et al., 2016;
19 Kusserow, 2017; Ibrahim et al., 2018; Dendoncker et al., 2020), consistent with, but not formally attributed
20 to climate change. In Algeria, mortality of Atlas cedar (Cedrus atlantica) increased from 1980 to 2006,
21 coinciding with a ~1ºC spring temperature increase, but non-climate factors were not examined (Navarro-
22 Cerrillo et al., 2019). Across southern Africa, nine of the 13 oldest known baobab trees (Adansonia digitata),
23 1100‒2500 years old, have died since 2005, although the causes are unknown (Patrut et al., 2018). In South
24 Africa, savanna trees experienced an order of magnitude increase in mortality, related, but not formally
25 attributed to decreased rainfall (Case et al., 2019). In Tunisia, insect infestations related, but not formally
26 attributed to, hotter temperatures led to mortality of cork oaks (Quercus suber) (Bellahirech et al., 2019).
27
28 2.4.4.3.3 Observed tree mortality in boreal and temperate ecosystems
29 The most extensive research on tree mortality since the IPCC Fifth Assessment Report has occurred in the
30 western United States, where anthropogenic climate change accounts for half the magnitude of a drought
31 from 2000 to 2020 that has been the most severe since the 1500s (Williams et al., 2020) and for one-tenth to
32 one-quarter of the magnitude of the 2012-2014 period of the severe 2012-2016 drought in California
33 (Williams et al., 2015a). Across the western United States, anthropogenic climate change doubled tree
34 mortality between 1955 and 2007 (van Mantgem et al., 2009). Lodgepole pine (Pinus contorta) mortality
35 increased 700% from 2000 to 2013 (Anderegg et al., 2015) and piñon pine (Pinus edulis) experienced over
36 50% mortality from 2002 to 2014 (Redmond et al., 2018). In California montane conifer forest,
37 anthropogenic climate change increased tree mortality one-quarter (Goulden and Bales, 2019). One-quarter
38 of trees died in some areas with mortality rates of ponderosa pine (Pinus ponderosa) and sugar pine (Pinus
39 lambertiana) increasing up to 700% of pre-drought rates (Stephenson et al., 2019; Stovall et al., 2019).
40 Substantial field evidence shows that anthropogenic climate change has caused extensive tree mortality in
41 North America (robust evidence, high agreement).
42
43 In western North America, increased infestations of bark beetles and other tree-feeding insects that benefit
44 from increased winter temperatures (IPCC AR6 WGI 3.3.1.1) and longer growing seasons (IPCC AR6 WGI
45 2.3.4.3.1) have killed drought-stressed trees (Section 2.4.2.1; Anderegg et al., 2015; Kolb et al., 2016; Lloret
46 and Kitzberger, 2018; Redmond et al., 2018; Stephens et al., 2018; Fettig et al., 2019; Restaino et al., 2019;
47 Stephenson et al., 2019). Increasing temperatures have allowed bark beetles to move further north and higher
48 in elevation, survive through the winter at sites where they would previously have died, and reproduce more
49 often (Raffa et al., 2008; Bentz et al., 2010; Jewett et al., 2011; Macfarlane et al., 2013; Raffa et al., 2013;
50 Hart et al., 2017; Stephenson et al., 2019; Teshome et al., 2020; Koontz et al., 2021). Under warmer
51 conditions, some insects that were previously innocuous have become important agents of tree mortality
52 (Stephenson et al., 2019; Trugman et al., 2021). Field observations show mixed effects of bark beetle-
53 induced tree mortality on subsequent fire-caused tree mortality (Andrus et al., 2016; Meigs et al., 2016;
54 Candau et al., 2018; Lucash et al., 2018; Talucci and Krawchuk, 2019; Wayman and Safford, 2021). From
55 1997 to 2018, ~5% of western U.S. forest area died from bark beetle infestations (Hicke et al., 2020). In
56 most circumstances, trees that have been weakened by drought are more vulnerable to being killed by bark
57 beetles (Anderegg et al., 2015; Kolb et al., 2016; Lloret and Kitzberger, 2018; Redmond et al., 2018;
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FINAL DRAFT Chapter 2 IPCC WGII Sixth Assessment Report
1 Stephens et al., 2018; Fettig et al., 2019; Restaino et al., 2019; Stephenson et al., 2019; Koontz et al., 2021).
2 Climate change has contributed to bark beetle infestations that have caused much of the tree mortality
3 in North America (robust evidence, high agreement) (Section 2.4.2.1).
4
5 Across Europe, rates of tree mortality in field inventories from 2000 to 2012 were highest in Spain, Bulgaria,
6 Sweden, and Finland, positively correlated to maximum winter temperature and inversely correlated to
7 spring precipitation (Neumann et al., 2017). Tree mortality in Austria, the Czech Republic, Germany,
8 Poland, Slovakia, and Switzerland doubled from 1984 to 2016, correlated to intensified logging and
9 increased temperatures (Senf et al., 2018). Drought-related tree mortality rates from 1987 to 2016 were
10 highest in Ukraine, Moldova, southern France, and Spain (Senf et al., 2020). Climate contributed to tree
11 mortality across Europe from 1958 to 2001 (Seidl et al., 2011). In addition, insect infestations related to
12 higher temperatures (Okland et al., 2019) have caused extensive mortality of Norway spruce (Picea abies)
13 across nine European countries (Marini et al., 2017; Mezei et al., 2017). Across the Mediterranean Basin, a
14 combination of drought, wildfire, pest infestations, and livestock grazing has driven tree mortality (Penuelas
15 and Sardans, 2021). Climate change has contributed to tree mortality in Europe (high agreement, medium
16 confidence). (Section 2.4.2.1)
17
18 2.4.4.3.4 Tree mortality and fauna
19 A global meta-analysis of 631 cases of bird and mammal abundance changes in areas of tree mortality found
20 increasing abundance in a set of 186 bird species with increasing mortality and no trend in mammal
21 abundance (Fleming et al., 2021). Ground-nesting, ground foraging, tree hole nesting, and bark foraging
22 increased most, while nectar-feeding and foliage-gleaning birds declined. Invertebrates, especially ground-
23 foraging predators and detritivores, decreased.
24
25 2.4.4.4 Observed Terrestrial Ecosystem Carbon
26
27 2.4.4.4.1 Observed terrestrial ecosystem carbon globally
28 Terrestrial ecosystems contain stocks of 450 Gt (380‒540 Gt) carbon in vegetation, 1700 Gt ± 250 Gt carbon
29 in soils, and 1400 Gt ± 200 Gt carbon in permafrost (Hugelius et al., 2014; Batjes, 2016; Jackson et al.,
30 2017; Strauss et al., 2017; Erb et al., 2018a; Xu et al., 2021). Ecosystem carbon stocks, totaling 3030–4090
31 GtC (from lowest and highest estimates above) substantially exceed the ~900 Gt carbon in unextracted fossil
32 fuels (see Chapter 5 of WGI).
33
34 Deforestation, draining of peatlands, expansion of agricultural fields, livestock pastures, and human
35 settlements, and other land use changes emitted carbon at a rate of 1.6 ± 0.7 Gt y-1 from 2010 to 2019,
36 (Friedlingstein et al., 2020), of which wildfires and peat burning emitted 0.4 ± 0.2 Gt y-1 from 1997 to 2016
37 (van der Werf et al., 2017). Anthropogenic climate change has caused a portion of these emissions through
38 increases in wildfire (Section 2.4.4.2.1) and tree mortality (Section 2.4.4.3.1) but the fraction of the total
39 remains unquantified. Land use change produced ~15% of global anthropogenic emissions, from fossil fuels
40 and land (Friedlingstein et al., 2020). Terrestrial ecosystems removed carbon from the atmosphere through
41 plant growth at a rate of -3.4 ± 0.9 Gt y-1 from 2010 to 2019 (Friedlingstein et al., 2020).
42
43 Tropical deforestation and draining and burning of peatlands produce almost all of the carbon emissions
44 from land use change (Houghton and Nassikas, 2017; Friedlingstein et al., 2020), while forest growth
45 accounts for two-thirds of ecosystem carbon removals from the atmosphere (Pugh et al., 2019b). Global
46 terrestrial ecosystems comprised a net sink of -1.9 ± 1.1 Gt y-1 from 2010 to 2019 (Friedlingstein et al.,
47 2020), mainly due to growth in forests (Harris et al., 2021; Xu et al., 2021), mitigating ~31% of global
48 emissions from fossil-fuel burning and land use change (Friedlingstein et al., 2020).
49
50 In summary, terrestrial ecosystems contain 3000-4000 Gt carbon in vegetation, permafrost, and soils, three
51 to five times the amount of carbon in unextracted fossil fuels, and 4.4 times the carbon currently in the
52 atmosphere (robust evidence, high agreement). Tropical deforestation, draining and burning of peatlands and
53 other land use changes emit 0.9-2.3 Gt y-1 of carbon, ~15% of global emissions from fossil fuel and
54 ecosystems (robust evidence, high agreement). Terrestrial ecosystems currently remove more carbon from
55 the atmosphere, 2.5-4.3 Gt y-1, than they emit, so tropical rainforests, Arctic permafrost, and other
56 ecosystems provide the global ecosystem service of naturally preventing carbon from contributing to climate
57 change (high confidence).
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1
2 2.4.4.4.2 Observed stocks in high-carbon terrestrial ecosystems
3 The ecosystem that attains the highest aboveground carbon density in the world is coast redwood (Sequoia
4 sempervirens) forest, in California, USA, with 2600 ± 100 t ha-1 carbon (Van Pelt et al., 2016). The
5 ecosystem with the second highest documented carbon density in the world is mountain ash (Eucalyptus
6 regnans) forest in Victoria, Australia, with ~1900 t ha-1 (Keith et al., 2009). Within the tropics, tropical
7 evergreen broadleaf forests (rainforests) in the Amazon, the Congo, and Indonesia attain the highest carbon
8 densities, reaching a maximum of 230 t ha-1 in the Amazon (Mitchard et al., 2014) and the Congo (Xu et al.,
9 2017). Temperature increases reduce tropical rainforest aboveground carbon density 9.1 t ha-1 per degree
10 Celsius, through reduced growth and increased mortality (Sullivan et al., 2020).
11
12 Tropical forests contain the largest vegetation carbon stock in the world, with 180‒250 Gt of above- and
13 belowground carbon (Saatchi et al., 2011; Baccini et al., 2012; Avitabile et al., 2016). The Amazon contains
14 a carbon stock of 45‒60 Gt (Baccini et al., 2012; Mitchard et al., 2014; Englund et al., 2017).
15
16 Ecosystems with high soil carbon densities include peat bogs in Ireland, with up to 3000 t ha-1 (Tomlinson,
17 2005), Cuvette Centrale swamp forest peatlands in Congo, with an average of ~2200 t ha-1 (Dargie et al.,
18 2017), Arctic tundra, with an average of ~900 t ha-1 (Tarnocai et al., 2009), and mangrove peatlands in
19 Kalimantan, Indonesia, with an average of 850 ± 320 t ha-1 (Murdiyarso et al., 2015). Arctic permafrost
20 contains 1400 Gt ± 200 Gt to 3 m depth, the largest soil carbon stock in the world (Hugelius et al., 2014).
21 Globally, peatlands contain 470‒620 Gt carbon (Page et al., 2011; Hodgkins et al., 2018) of which boreal
22 and temperate peatlands contain 415 ± 150 Gt (Hugelius et al., 2020) and tropical peatlands 80‒350 Gt (Page
23 et al., 2011; Dargie et al., 2017; Gumbricht et al., 2017; Ribeiro et al., 2021). Other analyses increase the
24 upper estimates for boreal and temperate peatlands to 800-1200 Gt (Nichols and Peteet, 2019; Mishra et al.,
25 2021b).
26
27 Tropical forests and Arctic permafrost contain the highest ecosystem carbon stocks in aboveground
28 vegetation and soil, respectively, in the world (robust evidence, high agreement). These ecosystems form
29 natural sinks that prevent the emission to the atmosphere of 1400-1800 Gt carbon that would otherwise
30 increase the magnitude of climate change (high confidence).
31
32 2.4.4.4.3 Biodiversity and observed terrestrial ecosystem carbon
33 High biodiversity and ecosystem carbon generally occur together, with rainforests in the Amazon, the
34 Congo, and Indonesia containing the largest aboveground vegetation carbon stocks (Saatchi et al., 2011;
35 Baccini et al., 2012; Avitabile et al., 2016) and the highest vascular plant species richness (Kreft and Jetz,
36 2007) in the world. Aboveground ecosystem carbon and animal species richness show high correlation but
37 also high spatial variability (Strassburg et al., 2010). Aboveground carbon is correlated to genus richness
38 globally (Cavanaugh et al., 2014), but to species richness only in local areas (Poorter et al., 2015; Sullivan et
39 al., 2017). Species richness generally increases vegetation productivity in the humid tropics while tree
40 abundance increases productivity in drier conditions (Madrigal-Gonzalez et al., 2020). Across the Amazon,
41 ~1% of tree species contain 50% of the aboveground carbon, due to abundance and maximum height (Fauset
42 et al., 2015). Aboveground carbon in tropical forest shows positive correlations to vertebrate species richness
43 (probability values not reported) (Deere et al., 2018; Di Marco et al., 2018). In logged and burned tropical
44 forest in Brazil, species richness of plants, birds, and beetles increased with carbon density up to ~100 t ha-1
45 (Ferreira et al., 2018).
46
47 National parks and other protected areas, which, in June 2021, covered 15.7% of global terrestrial area
48 (UNEP-WCMC, 2021), contain ~90 Gt carbon in vegetation and ~150 Gt carbon in soil (one-fifth and one-
49 tenth, respectively, of global stocks) and remove carbon from the atmosphere at a rate of ~0.5 Gt y-1 (one-
50 sixth of global removals) (Melillo et al., 2016). The most strictly protected areas contain carbon at higher
51 densities, but illegal deforestation and fires in some protected areas emit 38 ± 17 Mt y-1 globally (Collins and
52 Mitchard, 2017). In the Amazon, protected areas store more than half of the aboveground vegetation carbon
53 stock of the region but account for only one-tenth of net emissions (Walker et al., 2020). Conservation of
54 high biodiversity areas, particularly in protected areas, protects ecosystem carbon, prevents emissions to the
55 atmosphere, and reduces the magnitude of climate change (high confidence).
56
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1 2.4.4.4.4 Observed emissions and removals from high-carbon terrestrial ecosystems
2 Most global deforestation is occurring in tropical forests (Pan et al., 2011; Liu et al., 2015; Houghton and
3 Nassikas, 2017; Erb et al., 2018a; Li et al., 2018; Harris et al., 2021), primarily for clearing of agricultural
4 land (Hong et al., 2021), causing primary tropical forest to comprise a net source of carbon to the atmosphere
5 from 2001 to 2019 (emissions to the atmosphere 0.6 Gt y-1, removals from the atmosphere -0.5 Gt y-1, net 0.1
6 Gt y-1) (Harris et al., 2021). While wildfires emitted an average of 0.4 ± 0.2 Gt y-1 carbon from 1997 to 2016
7 (van der Werf et al., 2017), individual fire seasons can emit the same magnitude, such as the 0.4 Gt carbon
8 from the Amazon fires of 2007 (Aragao et al., 2018), 0.5 Gt carbon from the Amazon fires of 2015-2016
9 (Berenguer et al., 2021) and 0.2 Gt from the Australia fires of 2019-2020 (Shiraishi and Hirata, 2021). So,
10 wildfires account for up to one-third of annual average ecosystem carbon emissions, while major fire seasons
11 can emit up to two-thirds of global ecosystem carbon emissions (medium evidence, medium agreement).
12
13 Primary boreal and temperate forests also comprised net sources in the period 2001-2019, but, when
14 including all tree age classes, boreal, temperate, and tropical forests were net sinks, as growth exceeded
15 permanent forest cover losses (Harris et al., 2021), though boreal and temperate forests are much stronger
16 sinks (Pan et al., 2011; Liu et al., 2015; Houghton and Nassikas, 2017). Estimates of carbon removals from
17 remote sensing may provide more accurate estimates of boreal forest carbon balances than earth system
18 models, which overestimate regrowth after forest and timber (Wang et al., 2021a). Mortality of boreal forest
19 in British Columbia from mountain pine beetle infestations converted 374 000 km2 from a net carbon sink to
20 a net carbon source (Kurz et al., 2008). Modeling suggests that a potential increase in water-use efficiency
21 and regrowth could offset the losses in part of the forest mortality area (Giles-Hansen et al., 2021).
22
23 The Amazon as a whole was a net carbon emitter from 2003 to 2008 (Exbrayat and Williams, 2015; Yang et
24 al., 2018b), primarily due to expansion of agricultural and livestock areas, which caused over two-thirds of
25 deforestation from 1990 to 2005 (De Sy et al., 2015; De Sy et al., 2019). Four sites in the Amazon also
26 showed net carbon emissions from 2010 to 2018, from deforestation and fire (Gatti et al., 2021). In the
27 Amazon, deforestation emitted 0.17 ± 0.05 Gt y-1 carbon from 2001 to 2015 (Silva Junior et al., 2020) while
28 fires emitted 0.12 ± 0.14 Gt y-1 carbon from 2003 to 2015 (Aragao et al., 2018). An analysis of the Amazon
29 carbon loss from deforestation and degradation estimated a loss of 0.5 Gt y-1 from 2010 to 2019, with
30 degradation accounting for three-fourths (Qin et al., 2021). Intact old-growth Amazon rainforest has been a
31 net carbon sink (Hubau et al., 2020) but may have become a net carbon source from 2010 to 2019 (Qin et al.,
32 2021).
33
34 In Indonesia and Malaysia, draining and burning of peat swamp forests for oil palm plantations emitted 60 ‒
35 260 Mt y-1 carbon from 1990 to 2015, converting peatlands in that period from a carbon sink to a source
36 (Miettinen et al., 2017; Wijedasa et al., 2018; Cooper et al., 2020). Deforestation of mangrove forests
37 emitted 10‒30% of deforestation emissions in Indonesia from 1980 to 2005 (Donato et al., 2011; Murdiyarso
38 et al., 2015), even though mangroves comprised only 3% of Indonesia primary forest area in 2000 (Margono
39 et al., 2014; Murdiyarso et al., 2015).
40
41 In North America, wildfire emitted 0.1 ± 0.02 Gt y-1 of carbon from 1990 to 2012, but regrowth was slightly
42 greater to produce a net sink (Chen et al., 2017). In California, USA, two-thirds of the 70 Mt carbon
43 emissions from natural ecosystems from 2001 to 2010 came from the 6% of the area that burned (Gonzalez
44 et al., 2015). Anthropogenic climate change caused up to half of the burned area (Section 2.4.4.2.1).
45
46 In the Arctic, anthropogenic climate change has thawed permafrost (Guo et al., 2020), leading to carbon
47 emissions of 1.7 ± 0.8 Gt y-1 in the winter from 2003 to 2017 (Natali et al., 2019). Wildfires in Arctic tundra
48 in Alaska from ~1930 to 2010 caused up to 0.5 m of permafrost thaw (Brown et al., 2015), exposing
49 peatland carbon (Brown et al., 2015; Gibson et al., 2018), including soil carbon deposits up to 1600 years old
50 (Walker et al., 2019).
51
52 Tropical deforestation, draining and burning of peatlands, and thawing of Arctic permafrost due to climate
53 change have caused those ecosystems to emit more carbon to the atmosphere than they naturally remove
54 through vegetation growth (high confidence).
55
56 2.4.4.5 Observed Changes in Primary Productivity
57
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1 2.4.4.5.1 Observed changes in terrestrial primary productivity
2 The difference between photosynthesis by plants (gross primary productivity [GPP]) and plant energy use
3 through respiration is the net growth of plants (net primary productivity [NPP]), which removes CO2 from
4 the atmosphere and mitigates emissions from deforestation and other land use changes (Section 2.4.4.4).
5 Global terrestrial NPP has exceeded land use emissions since the early 2000s, making terrestrial ecosystems
6 a net carbon sink (Friedlingstein et al., 2020).
7
8 Global terrestrial NPP increased 6% from 1982 to 1999, through increased temperature and increased solar
9 radiation in the Amazon from decreased cloud cover (Nemani et al., 2003), then decreased 1% from 2000 to
10 2009, because of extensive droughts in the southern hemisphere (Zhao and Running, 2010). From 1999 to
11 2015, increased aridity caused extensive declines in the Normalized Difference Vegetation Index (NDVI)
12 globally, particularly semi-arid ecosystems (Huang et al., 2016), indicating widespread decreases in NPP
13 (Yuan et al., 2019).
14
15 Global terrestrial GPP increased 2% from 1951 to 2010 and continued increasing at least through 2016, with
16 increased atmospheric CO2 showing a greater influence than natural factors (Li et al., 2017; Fernandez-
17 Martinez et al., 2019; Liu et al., 2019a; Cai and Prentice, 2020; Melnikova and Sasai, 2020). Global forest
18 area increased 7% from 1982 to 2016, mainly from forest plantations and regrowth in boreal and temperate
19 forests in Asia and Europe (Song et al., 2018), while regrowth in secondary forests > 20 years old, mainly in
20 boreal, temperate, and sub-tropical regions, generated a net removal of 7.7 Gt y-1 CO2 from the atmosphere
21 from 2001 to 2019 (Harris et al., 2021). Vegetation growth that exceeds the modelled CO2 fertilisation, gaps
22 in field data, and incomplete knowledge of plant mortality and soil carbon responses introduce uncertainties
23 into quantifying the magnitude of CO2 fertilisation (Walker et al., 2021). A combination of CO2 fertilisation
24 of global vegetation and secondary forest regrowth has increased global vegetation productivity (medium
25 evidence, medium agreement).
26
27 The relative increase in GPP per unit of atmospheric CO2 increase declined from 1982 to 2015, indicating a
28 weakening of any CO2 fertilisation effect (Wang et al., 2020c). Increased growth from CO2 fertilisation has
29 begun to shorten the life span of trees due to a trade-off between growth rate and longevity, based on
30 analyses of tree rings of 110 species around the world (Brienen et al., 2020). Furthermore, water availability
31 controls the magnitude of NPP (Beer et al., 2010; Jung et al., 2017; Yu et al., 2017), including water from
32 precipitation (Beer et al., 2010), soil moisture (Stocker et al., 2019), groundwater storage (Humphrey et al.,
33 2018; Madani et al., 2020a), and atmospheric vapour (Novick et al., 2016; Madani et al., 2020b). Drought
34 stress reduced NPP across tropical forests from 2000 to 2015 (Zhang et al., 2019b) and GPP in the tropics
35 from 1982 to 2016 (Madani et al., 2020b). Drought stress has also reduced GPP in some semi-arid and arid
36 lands (Huang et al., 2016; Liu et al., 2019a). In addition, nitrogen and phosphorus constrain CO2 fertilisation
37 (Terrer et al., 2019), though phosphorus limitation of tropical tree growth is species-specific (Alvarez-Clare
38 et al., 2013; Thompson et al., 2019). NPP has decreased during some time periods and in some regions
39 where drought stress has exerted a greater influence than increased atmospheric CO2 (medium evidence, high
40 agreement).
41
42 2.4.4.5.2 Observed changes in freshwater ecosystem productivity
43 Temperature affects primary productivity through moderating phytoplankton growth rates, ice cover, thermal
44 stratification and growing season length (Rühland et al., 2015; Richardson et al., 2018). Global warming has
45 reinforced eutrophication, especially cyanobacteria blooms (Wagner and Adrian, 2009; Kosten et al., 2012;
46 O’Neil et al., 2012; De Senerpont Domis et al., 2013; Adrian et al., 2016; Visser et al., 2016; Huisman et al.,
47 2018) (very high confidence). Conversely, warming can reduce cyanobacteria in hypertrophic lakes
48 (Richardson et al., 2019). Freshwater cyanobacteria may benefit directly from elevated CO2 concentrations
49 (Visser et al., 2016; Ji et al., 2017; Huisman et al., 2018; Richardson et al., 2019).
50
51 Macrophyte growth in freshwaters is likely to increase with rising water temperatures, atmospheric CO2 and
52 precipitation (robust evidence, high agreement) (Dhir, 2015; Hossain et al., 2016; Short et al., 2016;
53 Reitsema et al., 2018). Nonetheless, primary productivity in rivers is variable and unpredictable (Bernhardt
54 et al., 2018) because seasonal variations in temperature and light are uncorrelated, frequent high flow events
55 reduce biomass of autotrophs and droughts can strand and desiccate autotrophs.
56
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1 In large, nutrient-poor lakes warming-induced prolonged thermal stratification can reduce primary
2 production (medium evidence) (Kraemer et al., 2017). Warming may reduce phytoplankton concentrations
3 when temperature-induced increases in consumption of phytoplankton outpace increases in phytoplankton
4 production (De Senerpont Domis et al., 2013). These decreases in productivity may be under-recognised
5 responses to climate change.
6
7 Summary: Evidence is robust for increase in primary production with warming trends, but increases or
8 declines of algae cannot entirely be attributed to climate change; they are lake specific and modulated
9 through weather conditions, lake morphology, salinity, land-use and restoration, and biotic interactions
10 (medium evidence, medium confidence) (O’Beirne et al., 2017; Velthuis et al., 2017; Rusak, 2018; Ho et al.,
11 2019).
12
13 2.4.5 Conclusions on Observed Impacts
14
15 The consistency of patterns of biological change with expectations from regional or global warming
16 processes, coupled with an understanding of underlying processes, the coherence of these patterns at both
17 regional and global scales, all form multiple lines of evidence (Parmesan et al., 2013) that it is very likely
18 that observed range shifts and phenological changes in individual species can be attributed to regional and
19 global climate changes (very high confidence) (Section 2.4.2, Table 2.2; Table 2.3; Table SM2.1; Parmesan
20 et al., 2013).
21
22 Global and regional meta-analyses of diverse systems, habitats and taxonomic groupings document that
23 approximately half of all species with long-term records have shifted their ranges poleward and/or upward in
24 elevation and ~2/3 have advanced their timing of spring events (phenology) (Section 2.4.2, Table 2.2;
25 Parmesan and Hanley, 2015; Parmesan, 2019). Changes in abundance tend to match predictions from climate
26 warming, with warm-adapted species significantly out-performing cold-adapted species in warming habitats
27 (Feeley et al., 2020) and the composition of local communities becoming more 'thermophilised' i.e.,
28 experiencing 'increase in relative abundance of heat-loving or heat-tolerant species' (Section 2.4.2.3; Cline et
29 al., 2013; Feeley et al., 2020).
30
31 New studies since AR5, with more sophisticated analyses designed to capture complex responses, indicate
32 that past estimates of the proportion of species impacted by recent climate change have been underestimates
33 due to their unspoken assumptions that local or regional warming should lead solely to poleward/upward
34 range shifts and advancements of spring timing (Duffy et al., 2019). More complex analyses have
35 documented cases of winter warming driving delayed spring timing of northern temperate species due to
36 chilling requirements, and increased precipitation driving species' range shifts downslope in elevation, and
37 eastward and westward in arid regions (high confidence). Further new studies have shown that phenological
38 changes have, in some cases, successfully compensated for local climate change and reduced degree of range
39 shifts (medium confidence). Limited number of studies of this type make it difficult to estimate the generality
40 of these effects globally (Section 2.4.2.5, Table 2.2).
41
42 Responses in freshwater species are consistent with responses in terrestrial species, including poleward and
43 upward ranges shifts, earlier timing of spring plankton development, earlier spawning in fish, and extension
44 of the growing season. Observed changes in freshwater species are strongly related to anthropogenic climate
45 change driven changes in the physical environment (e.g. increased water temperature, reduced ice cover,
46 reduced mixing in lakes, loss of oxygen, reduction in river connectivity). While evidence is high for an
47 increase in primary production in nutrient rich lakes with warming trends (high confidence), increasing or
48 declining algal formations are lake specific and modulated through variability in weather conditions, lake
49 morphology, changes in salinity, stoichiometry, land-use and restoration measures, and food web
50 interactions. In boreal coniferous forest, there has been an increase in terrestrial derived dissolved organic
51 matter transported into rivers and lakes as a consequence of climate change (that has induced increases in
52 run-off and greening of the northern hemisphere), as well as to changes in forestry practices. This has caused
53 waters to become brown resulting in an acceleration of upper water warming and an overall cooling of deep
54 water (high confidence). Browning may accelerate primary production through input of nutrients associated
55 with DOM in nutrient poor lakes and increase cyanobacteria growth, which better cope with low light
56 intensities (medium confidence) (Sections 2.4.2.1, 2.4.2.2, 2.4.2.3, 2.4.2.4).
57
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1 Field research since the IPCC Fifth Assessment Report has detected biome shifts at numerous sites,
2 poleward and upslope, that are consistent with increased temperatures and altered precipitation patterns
3 driven by climate change, and support prior studies that attributed such shifts to anthropogenic climate
4 change (high confidence). These new studies help fill prior geographic and habitat gaps, for example
5 documenting upward shifts in the forest/alpine tundra ecotone in the Andes, Tibet and Nepal, and northward
6 shifts in the deciduous/boreal forest ecotones in Canada. Globally, woody encroachment into open areas
7 (grasslands, arid regions and tundra) is likely being driven by climate change and increased CO2 in concert
8 with changes in grazing and fire regime (medium confidence) (Section 2.4.3).
9
10 Climate change has driven or is contributing to increased tree mortality directly through increased aridity or
11 drought and indirectly through increased wildfire and insect pests in many locations (high confidence).
12 Analyses of causal factors have attributed increasing tree mortality at sites in Africa and North America to
13 anthropogenic climate change and field evidence has detected tree mortality from drought, wildfire, and
14 insect pests in temperate and tropical forests around the world (high confidence). Water stress, leading to
15 plant hydraulic failure, is the a principal mechanism of drought-induced tree mortality, along with indirect
16 effects of climate change mediated through community interactions (high confidence) ( Section 2.4.4.3).
17
18 Terrestrial ecosystems sequester and store globally critical stocks of carbon but these stocks are at risk from
19 deforestation and climate change (high confidence). Tropical deforestation, draining, and burning of
20 peatlands produce almost all of the carbon emissions from land use change. In the Arctic, increased
21 temperatures have thawed permafrost at numerous sites, dried some areas, and increased fire, causing net
22 emissions of carbon from soils (high confidence) (Sections 2.4.4.4, 2.5.3.4).
23
24 Globally, increases in temperature, aridity, and drought have increased the length of fire seasons and doubled
25 potentially burnable area (medium confidence). Increases in burnt area have been attributed to anthropogenic
26 climate change in North America (high confidence). In parts of Africa, Asia, Australia, and South America,
27 area burned have also increased, consistent with anthropogenic climate change. Deforestation, peat burning,
28 agricultural expansion or abandonment, fire suppression, and inter-decadal cycles, strongly influence fire
29 occurrence. Areas with the greatest increases in fire season length include the Amazon, western North
30 America, western Asia, and East Africa. (Section 2.4.4.2)
31
32 The changes we have observed, and project to continue, in biodiversity and ecosystem health pose a risk of
33 declines in human health and well-being: e.g. tourism, recreation, food, livelihoods and quality of life
34 (medium confidence). Clear attribution of these impacts is often not possible, but inference can be made by
35 comparison of observed changes in biodiversity / ecosystem health and known services from those particular
36 ecosystems.
37
38
39 Table 2.3: Confidence in detecting and attributing observed changes in terrestrial and freshwater species and systems to
40 climate change. Lines of evidence for attribution of observed changes to climate change and increased CO2 are used to
41 support stated confidence in attribution of key statements on observed biological changes to climate change and
42 increased atmospheric CO2. Icons represent lines of evidence. This is a summary table that is fully detailed in Table
43 SM2.1 in Supplementary Material.
44 Lines of evidence:
Paleo data Experiment Long-term observations
Fingerprint of climate Models Complex statistical
change response analysis
Key statement Region Period Lines of evidence Climate change
attribution
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About half of all species where land use Global Range 20 - 260
change has been minimal have shifted their years robust evidence
ranges, with 80‒90% of movements being high agreement
in the direction expected from regional very high
warming trends ‒ i.e. poleward and confidence
upward.
Downslope elevational shifts and east-west USA ~ 40‒60 years
shifts (shown for trees and birds) have been limited evidence,
associated with regional increases in high agreement,
precipitation where precipitation has been medium confidence
shown to be the principal driver of a range
boundary
About two- thirds of all species with long- Global Varies by study.
term (>20 years) records have shifted the Range: 20‒400 robust evidence
timing of spring events in directions years high agreement
expected from regional winter and spring very high
warming. confidence
Winter chilling-depending species have Northern Varies by study.
delayed or not changed spring events Europe Range = 26‒46 medium evidence
despite spring warming countered by and USA years high agreement
winter warming. When these species are high confidence
taken into account, it is estimated that 92%
of species in these studies have responded
to regional warming trends
Anthropogenic climate change, acting western 1984-2017
through increased heat and aridity at global north robust evidence
mean surface temperature increases of 0.6- America high agreement
0.9ºC, has increased the area burned by high confidence
wildfire over natural levels, increasing
burned area up to 11 times in one extreme
year and doubling over natural levels in a
32-year period
Anthropogenic climate change has caused North ca. 1945-2007
drought-induced tree mortality of up to America medium evidence
20% in three regions, through global mean and Africa high agreement
surface temperature increases of 0.3-0.9ºC medium confidence
above the pre-industrial period and
increases in aridity, more than non-climate
change factors
Anthropogenic climate change has caused Global 1500-2007
latitudinal and elevational vegetation biome
shifts in at least 19 sites in boreal, robust evidence
temperate, and tropical ecosystems, high agreement
between 1700 and 2007, through local high confidence
temperature increases of 0.4 to 1.6ºC above
the pre-industrial period more than non-
climate change factors
Anthropogenic climate change and wildfire western 1966-2015 medium evidence
together have altered vegetation species North high agreement
composition in at least two regions, America, medium confidence
reducing post-fire natural regeneration and Africa
species richness of tree and other plant
species, at global mean surface temperature
increases of 0.3-0.9ºC
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Beetles & moths shifting poleward and North Varies by study
upward has brought new pest species into America, medium-high
some forests; warming winters and longer Europe confidence
growing season has increased destructive
outbreaks of beetles and moths in temperate
and boreal forests
Exotic species are responding differently North
from native species in both abundance America low/medium
changes and phenological changes, but not evidence
in a consistent fashion low agreement
The most cold-adapted species are Arctic,
generally declining in population Himalayas
abundances and contracting their ranges , medium confidence
poleward and upward: (e.g. sea-ice Antarctic,
dependent, mountain-top restricted, upper Alps
headwaters, coldest lakes)
Diseases of both wildlife and humans have Global
emerged into new areas they have not been past 20-100 medium confidence
in historically years
Warming has amplified the trophic state Global Past 20-50
lakes are already in. Eutrophic lakes have years robust evidence
become more productive while oligotrophic high agreement
lakes tend to become more nutrient limited high/medium
confidence
Woody encroachment into open (grassland, Global
desert) systems has occurred, with climate
change as one of the drivers, along with medium confidence
changes in grazing and other land uses
In boreal, coniferous areas changes in Boreal Past decades
forestry practices and climate change have
caused an increase in terrestrial derived robust evidence
dissolved organic matter (DOM) transport high agreement
into rivers and lakes leading to their high confidence
browning
Climate change induced warming leads to Global Past decades
shifts in thermal regime of lakes robust evidence
high agreement
high confidence
Climate change causes gains and losses in Global Past decades limited evidence
freshwater water level low confidence
Greenhouse gas emissions from freshwater Global Past decades medium evidence
ecosystems are equivalent to around 20% of medium agreement
global burning fossil-fuel CO2 emission medium/low
confidence
In lakes weather extremes in wind, North Varies by study medium/limited
temperature, precipitation and loss of ice America, evidence
foremost affect the thermal regime with Europe high agreement
repercussions on water temperature, medium/low
transparency, oxygen and nutrient confidence
dynamics, affecting ecosystem functionality
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Climate change induced warming leads to North Past decades
shifts in thermal regime of rivers and America, robust evidence
streams; lowland rivers show a stronger Europe medium agreement
thermal response than high-altitude, cold- high confidence
water receiving streams
Loss of biodiversity in streams can be Global Past decades
directly attributed to climate change high agreement
through increased water temperatures, very high
hydrological changes such as increased confidence
peak discharges, flow alteration and
droughts
Climate change is causing range shifts of North Past decades high agreement,
freshwater fish America high confidence
Europe
1
2
3 2.5 Projected Impacts and Risk for Species, Communities, Biomes, Key Ecosystems and their
4 Services
5
6 Under the Risk Assessment Framework that was introduced in AR5 (2014), risk means the probability of
7 harmful consequences resulting from climate change. It results from the interaction of vulnerability,
8 exposure, and hazard (see Chapter 1) and can be represented as the probability of occurrence of hazardous
9 events or trends multiplied by the impacts if these events or trends occur (IPCC, 2014). The Framework
10 defines vulnerability as a pre-existing condition, incorporating the extent to which species or ecosystems are
11 susceptible to, or unable to cope with, adverse effects of climate change. Vulnerable species have limited
12 adaptive capacity stemming from physiological and behavioural constraints, limited dispersal abilities and
13 restricted resource requirements or capacities for distributional and genetic changes (Foden et al., 2019)
14 (Foden et al., 2013; Cizauskas et al., 2017). Traits that render entire ecosystems vulnerable are harder to
15 define, but it is clear that vulnerabilities are high in the coldest habitats, in those with limited geographic
16 ranges such as low-lying islands, and in specialized, restricted habitats such as serpentine outcrops in
17 California (Anacker and Harrison, 2012) and dry meadows in Fennoscandia and Tibet (Yang et al., 2018a).
18 Ecosystem vulnerability can depend critically on the fates of plants that function as 'foundation species,'
19 providing community biomass aboveground and below, structuring habitat for fauna and providing
20 ecosystem services such as erosion control (Camac et al., 2021).
21
22 2.5.1 Projected Changes at Species and Community Levels
23
24 2.5.1.1 Assessment of Models and Sources of Uncertainties
25
26 Methods for projecting impacts of climate change on biodiversity can be classified into three types: 1)
27 statistical models such as species distribution models (Elith and Leathwick, 2009); 2) mechanistic or
28 process–based models (Chuine and Régnière, 2017) and 3) trait–based models (Pacifici et al., 2015). It is
29 only recently that models have been developed looking at smaller levels of warming. such as 1.5°C (Hoegh-
30 Guldberg et al., 2018a; Warren et al., 2018).
31
32 Species distribution models (SDMs) or niche-based models assess potential geographic areas of suitable
33 climate for the species in current conditions and then project them into future conditions (Trisurat, 2018;
34 Vieira et al., 2018). There are limitations in all models and it is critical that modellers understand the
35 assumptions, proper parameterization, and limitations of each model technique, including differences among
36 climate models, emission scenarios or representative concentration pathways, and baselines (Araujo et al.,
37 2019). Several systems automate development of SDMs, including R-packages (e.g., (Beaumont et al., 2016;
38 Hallgren et al., 2016), development of other model types (Foden et al., 2019) and aid in use of climate model
39 data (Suggitt et al., 2017), including allowing for connectivity constraints (Peterson et al., 2013). Buisson et
40 al. (2010) found most variation in model outputs stem from differences in design, followed by GCMs.
41
42 Mechanistic approaches, also known as process-based models, project species’ responses to climate changes
43 by explicitly incorporating known biological processes, thresholds and interactions (Morin and Thuiller,
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1 2009; Maino et al., 2016). Mechanistic models are able to accommodate a broad range of climate change
2 impact mechanisms and include species-specific characteristics such as dispersal distances, longevity,
3 fecundity, genetic evolution, phenotypic plasticity. However, sufficient knowledge is available for only a
4 few well-studied species. Species traits have been used to more broadly estimate potential climate change
5 impacts (Foden et al., 2013; Cizauskas et al., 2017).
6
7 Most models are at large scales (20 km–50 km), and so cannot capture micro-climatic refugia generated by
8 diversities of slope aspect, elevation or shade (Suggitt et al., 2015; Suggitt et al., 2018). In analyzing records
9 of 430 climate-threatened and range-declining species in England, (Suggitt et al., 2015; Suggitt et al., 2018)
10 showed that topographic diversity reduced population declines most strongly in areas experiencing the most
11 local warming and in species most sensitive to warming. In these circumstances topographic diversity
12 reduced population extinction risk by 22% for plants and by 9% for insects.
13
14 None of the modelling techniques are predictions of the future; they are projections of possible futures. To
15 date, few studies have validated model performance against observations, and many of these have been on
16 islands, reducing ability to generalize (Fordham et al., 2018). Species' models should be considered as
17 hypotheses of what a future world might look like if the climate projections came to pass. Suggestions have
18 been made on how to start bringing more biotic interactions into species distribution models (Early and
19 Keith, 2019), but limited basic ecological understandings of interactions, along with limits on computation
20 and funding, constrain how far and how fast these modelling techniques can advance.
21
22 2.5.1.2 Risk Assessment and non-modelling approaches
23
24 In order to add realism and reliability to risk assessments at the species and community levels, non-
25 modelling approaches based on known biological traits or processes, as well as on expert opinion (Camac et
26 al., 2021), are used to temper model outputs with ground-based validation. Trait-based assessment
27 approaches use species’ biological characteristics as predictors of sensitivity, adaptive capacity and
28 extinction risk due to climate change. Climate exposure can be estimated using GIS-based modelling,
29 statistical programs or expert judgment (Chin et al., 2010). These trait-based approaches are widely applied
30 to predict responses of biodiversity to climate change because they do not require modelling expertise nor
31 detailed distibutional data (Pacifici et al., 2015; Willis et al., 2015). Most of these methods have not been
32 independently validated and do not allow direct comparison of vulnerability and risk among taxonomic
33 groups.
34
35 Some studies have combined two or three approaches for climate change risk assessment of biodiversity in
36 order to capture the advantages of each and avoid their limitations. Warren et al. (2013) used combinations
37 of SDMs and trait-based approaches to estimate the proportions of species losing their climatically suitable
38 ranges under the various future scenarios of climate and dispersal rate. Similarly, spatial projections of
39 climate change exposure were combined with traits to assess vulnerability of sub-Saharan amphibians
40 (Garcia et al., 2014). Laurance et al. (2012) combined 31 functional groups of species and 21 potential
41 drivers of environmental change to assess both the ecological integrity and threats for tropical protected
42 areas on a global scale. Keith et al. (2014) used a combination of three approaches (SDMs-trait-mechanistic)
43 to determine how long before extinction a species would become eligible for listing as threatened based on
44 the IUCN Red List criteria.
45
46 2.5.1.3 Risk of Species’ Extinctions
47
48 2.5.1.3.1 Overview
49 This assessment of current findings is of studies across a range of taxa and modelling techniques. Extinction
50 risk estimates whether or not a particular species may be at risk of extinction over the coming decades if
51 climatic trends continue and usually does not take into account other human-induced stressors (e.g. invasive
52 species or pollution). It is not a prediction that a species will definitely go extinct, because even complete
53 loss of a species’ range is projected, the scale of the model cannot estimate persistence in micro-climatic
54 refugia (Suggitt et al., 2015; Suggitt et al., 2018). Individuals and populations can survive after conditions
55 for successful reproduction are gone, leading to a lagged decline, called 'extinction debt' (Alexander et al.,
56 2018). Therefore, range loss is an established criterion for assessing endangerment status and risk of
57 extinction. As a species range becomes smaller and occupied habitats become more isolated, the likelihood
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1 of a single stochastic event causing extinction increases. It is this combination of projected loss of
2 climatically suitable space and additional stressors (especially LULCC of critical habitat) that is expected to
3 drive future extinctions.
4
5 The IUCN Red List Criteria (IUCN, 2019) classifies a species as 'critically endangered' if it has suffered a
6 range loss of ≥80%, with a resulting likelihood of extinction of >50% in the near term (10-100 years,
7 depending upon generation time). A species is classified as 'endangered' if it has suffered a range loss of
8 ≥50%, with a resulting likelihood of extinction of >20% in the near term (10-100 years).
9
10 2.5.1.3.2 Projections for freshwater biodiversity
11 Lakes, rivers and freshwater wetlands cover approximately 7.7–9.1 % of global land surface area; (Lehner et
12 al., 2008; Fluet-Chouinard et al., 2015; Allen and Pavelsky, 2018), and hold 9.5% of the Earth’s described
13 animals (Balian et al., 2008), with climate change indicated as a threat for 50–75% of fish (Xenopoulos et
14 al., 2005; Darwall and Freyhof, 2015 who). Climate change is cited as a primary factor in species' extinction
15 risk, through changes in water temperatures, stream flow, loss of cold water habitat, increased variability of
16 precipitation, and increased disease risk from warming temperatures (high evidence, high agreement, high
17 confidence) (Knouft and Ficklin, 2017; Pletterbauer et al., 2018; Reid et al., 2019; Jaric et al., 2019), adding
18 to stress from overexploitation and LULCC (Craig et al., 2017; IPBES, 2019 Global assessment report).
19
20 Increased frequency of stream drying events, reducing hydrologic connectivity and limiting access of native
21 fishes to spawning habitats is projected for RCP 8.5 in Colorado, USA (medium evidence, medium
22 agreement) (Jaeger et al., 2014). Cold-water habitats and associated obligate species are particularly
23 vulnerable and losses in these habitats have been both documented and projected, for example in salmonids
24 (Santiago et al., 2016; Fullerton et al., 2017; Merriam et al., 2017). River networks are projected to lose
25 connections to cold tributary refugia, that are important thermal refuges for cold water species (robust
26 evidence, high agreement) (Isaak et al., 2016) during low flows (Merriam et al., 2017).
27
28 Community turnovers are expected in freshwaters as cold-adapted species lose and warm-adapted species
29 gain climatically suitable habitat (Domisch et al., 2011; Domisch et al., 2013; Shah et al., 2014). While a
30 number of warm-adapted species may experience range expansions, the majority of species are predicted to
31 lose climatically suitable areas by on average 38–44%, depending on the emission scenario (A2a and B2a)
32 (medium evidence) (Domisch et al., 2013).
33
34 Molluscs are projected to be the most at risk group, given their limited dispersal capability (Woodward et al.,
35 2010). Mediterranean freshwater fish are especially susceptible to climate change due to increasing flood and
36 drought events and risks of surpassing critical temperature thresholds (Santiago et al., 2016; Jaric et al.,
37 2019). In southern Europe, aquatic insects (Ephemeroptera, Plecoptera, and Trichoptera) are endangered by
38 climate change (Conti et al., 2014). European protected areas are not expected to be sufficient under
39 warming to provide habitat for the majority of rare molluscs and fish (Markovic et al., 2014). Observed
40 trends agree with model projections in direction, but magnitude remains uncertain (medium evidence,
41 medium agreement, medium confidence). (see Figure 2.8 for extinction risk globally for dragonflies,
42 amphibians and turtles).
43
44 Regional threats from climate change have been reported for 40% of amphibians in China, (Wu, 2020), 33%
45 of European freshwater fish species (Janssen et al., 2016) and 56-69% of Odonates in Australia, (Bush et al.,
46 2014b). Assessment of site-specific extirpation likelihoods for 88 aquatic insect taxa projected that climate-
47 change induced hydrological alteration would result in a 30–40% loss of taxa in warmer, drier ecoregions
48 and 10–20% loss in cooler, wetter ecoregions (medium evidence) (Pyne and Poff, 2017). In Africa’s
49 Albertine Rift, 51% (n=551) of fish are expected to be impacted by climate change, with 5.5% at high risk
50 due to their sensitivity and poor adaptative capability (high agreement) (Carr et al., 2013).
51
52 The GLOBIO-Aquatic model (Janse et al., 2015 a) links models for demography, economy, land use
53 changes, climate change, nutrient emissions, a global hydrological model and a global map of water bodies.
54 It projects that changes in both water quality (eutrophication ) and quantity (flow) will generate negative
55 relations in freshwater ecosystems between persistence of species originally present in each community and
56 a constellation of stressors, including harmful algal blooms. Under 4°C rise by 2050, mean abundance of
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1 species is projected to decline by 70% in running water and 80% in standing water (medium evidence, high
2 agreement, medium confidence) (Janse et al., 2015 a).
3
4 2.5.1.3.3 Global projections of extinction risk
5 In prior reports, risk assessed from the literature was generally based on estimates of overall range
6 contractions with climate change. In AR4, extinction risk was carefully quantified: 'There is medium
7 confidence that approximately 20–30% of species assessed so far are likely to be at increased risk of
8 extinction if increases in global average warming exceed 1.5–2.5°C (relative to 1980 to 1999). As global
9 average temperature increase exceeds about 3.5°C, model projections suggest significant extinctions (40–
10 70% of species assessed) around the globe.' These estimates approximately correspond to 50% reductions in
11 range size (IPCC, 2007a). AR5 stated 'a large fraction of terrestrial and freshwater species face increased
12 extinction risk under projected climate change during and beyond the 21st century, especially as climate
13 change interacts with other pressures ... (high confidence)' (Field et al., 2014). A series of multi-species and
14 global analyses have been published since AR5, using both statistical models and trait-based approaches.
15
16 In this Chapter, risk to species, with implications for ecosystems, is assessed using three different
17 approaches. First is an assessment of the geographic distributions of species' losses at different levels of
18 GAST warming, termed 'biodiversity loss, measured as the proportion of species within a given location
19 becoming classified as 'endangered' or 'critically endangered' (sensu IUCN). This measure provides estimates
20 of which sites are at most risk of losing substantial numbers of species locally, leading to degradation of that
21 ecosystems' ability to function. Second is an assessment of risk of proportions of species' becoming extinct
22 globally at different levels of GAST warming, measured using the IUCN criteria for 'critically endangered',
23 and termed 'species' extinction risk'. This measure is closest to assessing the complete loss of a species in the
24 wild, and can be used to compare to past (paleo) extinction rates. Third is an assessment of proportions of
25 species becoming rare or endangered globally (not just locally), and is the foundation for the Burning
26 Embers on biodiversity risk in Figure 2.11. These three approaches provide complementary information of
27 the overall risks to biodiversity and ecosystem integrity under different warming levels.
28
29 Biodiversity risk, estimated as the proportion of species in a given area projected to become endangered
30 (sensu IUCN), is projected to affect a greater number of regions with increasing warming, with about one-
31 third of land area risking loss of >50% of species currently inhabiting those ecosystems. Species' losses are
32 projected to be worst in the northern South America, southern Africa, most of Australia, and northern high
33 latitudes (medium confidence) (Figure 2.6).
34
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1
2 Figure 2.6: Biodiversity loss at increasing levels of climate change. The higher the percent of species projected to
3 lose suitable climate in a given area, the higher the risk to ecosystem integrity, functioning and resilience to climate
4 change. Warming levels are based on global levels (GSAT) above pre-industrial temperatures. Colour shading represent
5 proportion of species for which the climate is projected to become unsuitable within a given pixel across their current
6 distributions at a given GSAT warming level, based on the data underpinning (Warren et al., 2018) (modelled
7 n=119,813 species globally, with no dispersal, averaged over 21 CMIP5 climate models). Areas shaded in green are
8 above the 50% biodiversity loss threshold, meaning that <50% of species in that area are projected to go locally extinct.
9 Areas shaded in pink and purple represent significant risk of biodiversity loss (areas where climates become unsuitable,
10 rendering them locally extinct, for >50% and >75% of species, respectively). The maps of species richness remaining
11 have been overlaid with a landcover layer (2015) from the European Space Agency Climate Change Initiative. This
12 landcover layer leaves habitats classified by the ESA as natural as being transparent, cities as black, water as blue,
13 permanent snow/ice as white and bare/rock as dark brown. Areas with a landcover identified as agriculture are 5%
14 transparent, such that potential species richness remaining if the land had not been converted to agricultural shows as
15 pale shading of the legend colours (very pale pink or very pale green). These paler areas represent biodiversity loss due
16 to habitat destruction, but with a potential to be restored, with green shading having potential for restoration to higher
17 species richness than pink and purple shadings.
18
19
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1 Risk of species' becoming extinct globally was estimated as the probability of loss of suitable climate space
2 rendering it critically endangered (sensu IUCN). It is likely that the percentage of species at high risk of
3 extinction (median and maximum estimates) will be 9% (max 14%) at 1.5°C, 10% (max 18%) at 2°C, 12%
4 (max 29%) at 3.0°C, 13% (max 39%) at 4°C and 15% (max 48%) at 5°C (Figure 2.7). Among groups
5 containing largest numbers of species at high risk of extinctions for mid-levels of projected warming (3.2°C)
6 are: invertebrates (15%), specifically pollinators (12%), amphibians (11%, but salamanders are at 24%) and
7 flowering plants (10%) (Figure 2.8a). All groups fare substantially better at 2°C, with extinction
8 projectionsreducing to <3% for all groups, except salamanders at 7% (medium confidence) (Figure 2.8a).
9 Even the lowest estimates of species' extinctions (9%) are 1000x natural background rates (Section 2.5.4; De
10 Vos et al., 2015). Projected species' extinctions at future global warming levels are in accord with
11 projections from AR4, assessed on much larger numbers of species with much greater geographic coverage
12 and a broader range of climate models. (Figure 2.7; Figure 2.8)
13
14
15
16 Figure 2.7: Synthesis of modelled climate-driven extinction risk studies. The relationship between modelled
17 projections of extinction (expressed as a proportion of species at risk of extinction assessed in individual studies) and
18 GSAT increase above the pre-industrial average. Data (global sample size n = 178 modelled estimates) were sourced
19 from a number of sources, including digitization of data points in Figure 2 in the synthetic analysis of (Urban, 2015),
20 note: unweighted for sample size, n = 126); Table 4.1 of the AR4 WG2 chapter 2 (Fischlin et al., 2007), n = 40;
21 (Hannah et al., 2020) n = 6); and (Warren et al., 2018) n = 6). The. Quantile regression (which is robust to the non-
22 normal distribution of the response variable, and less sensitive to data outliers) was used to fit quantile estimates for
23 levels relevant to informing “likely” (between the 0.17 and 0.83 quantiles, shaded in orange) and “very likely” ranges
24 (between the 0.05 and 0.95 quantiles, shaded in green) relating extinction risk to GSAT increase (Quantile regression
25 implemented using the Barrodale and Roberts algorithm in XLSTAT). The roughly equivalent estimate of this risk as
26 expressed in AR4 (Fischlin et al., 2007) is indicated by the dotted block indicating the medium confidence statement
27 “Approximately 20 to 30% of plant and animal species assessed so far (in an unbiased sample) are likely to be at
28 increasingly high risk of extinction as global mean temperatures exceed a warming of 2 to 3°C above preindustrial
29 levels (medium confidence).” This box is open on the right-side because AR4 estimates stipulated temperatures at or
30 exceeding given levels.
31
32
33 Projections of extinction risk by taxa are presented both for risk of becoming critically endangered (losing
34 ≥80% of suitable climate habitat, Figure 2.8a) and endangered (losing ≥50% of suitable climate habitat,
35 Figure 2.8b). The percentages of species projected at risk of becoming endangered (or worse) was 49% for
36 insects, 44% for plants, and 26% for vertebrates at ~3°C global rise in temperature (Warren et al., 2018).
37 Those estimates dropped considerably at lower levels of warming, down to 18%, 16%, and 8% at 2°C; and
38 6%, 8% and 4% at 1.5°C (Warren et al., 2018); thus not entirely dis-similar to the numbers in AR4 (Figure
39 2.7). Figure 2.8 shows the benefits of dispersal in offsetting extinction risk in birds, mammals, butterflies,
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1 moths and dragonflies. While dispersal may benefit individual species, it poses additional risks to
2 communities and ecosystems as interactions between species are changed or eliminated.
3
4
5
6 Figure 2.8: Percent of species of different groups classified as being under risk of extinction. a) Percent of the species
7 group listed projected to be at very high risk of extinction, corresponding to the IUCN Red List criteria for a species
8 classified as "critically endangered" (version 3.1) through losing >80% of its climatically suitable range area. b) Percent
9 of the species group listed projected to be at high risk of extinction, corresponding to the IUCN Red List criteria for a
10 species classified as "endangered" (version 3.1) through losing >50% of its climatically suitable range area. For a) and
11 b), values calculated from the underlying data underpinning (Warren et al., 2018). Values for each temperature are the
12 mean values across 21 CMIP5 models. The grey band represents the high-end of extinction risk from the 10th
13 percentile of the climate models to show the maximum range of values while the low end (90th percentile, 1.5°C) is not
14 shown as it is too small to appear on the plots. Taxa marked with * represent potential benefits from adaptation,
15 specifically dispersal at realistic rates (Warren et al., 2018); those with no * have dispersal rates that are essentially not
16 detected in the spatial resolution of the models (20 km). See (Warren et al., 2018) for caveats and more details. Sample
17 size for each group is as follows: Invertebrates (33949), Annelid Worms (155), Butterflies (1684), Moths (6910),
18 Dragonflies (599), Pollinators (1755), Spiders (2212), Beetles (7630), True Bugs (1728), Bees/Ants/Wasps (5914),
19 Flies (4809), Plants (72399), Flowering Plants (52310), Conifers (340), Timber spp (1328), Grasses (3389), Fungi
20 (16187), Vertebrates (12642), Mammals (1769), Carnivores (107), Ungulates (80), Bats (500), Birds (7968),
21 Passeriformes (4744), Non-passeriformes (3224), Amphibians (1055), Frogs (887), Salamanders (163), Reptiles (1850),
22 Snakes (1741), Turtles (94).
23
24
25 For local biodiversity loss, at 1.58°C (median estimate), >10% of species are projected to become
26 endangered (sensu IUCN); at 2.07°C (median) >20% of species are projected to become endangered,
27 representing high and very high biodiversity risk, respectively (medium confidence) (see Section 2.5.4;
28 Figure 2.11; Table 2.5, Table 2.S.4).
29
30 Using data from geological time scales, Song et al. (2021) predicted that a warming of 5.2 °C above pre-
31 industrial would result in mass extinction comparable to that of the five mass extinctions over the past 540
32 My, on the order of 70–85% of species going extinct, in the absence of non-climatic stressor. Mathes et al.
33 (2021) found evidence in the geological record that short-term rapid warming, on top of long-term warming
34 trends, increases extinction risk by up to 40% over that expected from the long-term trend alone, with a
35 biodiversity 'memory' up to 60 Myr, indicating an additonal risk of multi-decadal overshoot.
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1
2 Most of the large-scale studies that have been performed are for losses based on climate alone (Figures 2.6,
3 2.7, 2.8). However, climate is rarely the only stressor affecting species survival. Habitat loss is currently the
4 largest driver of range loss and extinction risk for most species (IUCN). Communities in different regions are
5 becoming more similar to each other as species tolerant of human activities prosper and spread, with many
6 rare and endemic species already having been driven extinct, primarily by LULCC (Pimm et al., 2006).
7 Thus, it will likely be the interaction of climate change and habitat conversion (often also being driven by
8 climate change) that will ultimately determine the risk and ability to survive of many species.
9
10 2.5.1.4 Changing Risks of Diseases
11
12 Multiple studies predict increases in disease incidence or geographic and phenological changes of pathogens,
13 vectors, and reservoir host species due to climate change with or without other non-climatic variables
14 (González et al., 2010; Moo-Llanes et al., 2013; Roy-Dufresne et al., 2013; Liu-Helmersson et al., 2014;
15 Laporta et al., 2015; Ryan et al., 2015; Haydock et al., 2016; Hoover and Barker, 2016; Prist et al., 2017;
16 Blum and Hotez, 2018; Dumic and Severnini, 2018; Hundessa et al., 2018; Ryan et al., 2019; Ryan et al.,
17 2021). However, models predicting changes in infectious disease risk are complex and sometimes produce
18 conflicting results and lack consensus (Caminade et al., 2014; Giesen et al., 2020). For example, malaria is
19 projected to increase in some regions of Africa, Asia, and South America by the end of the 21st century if
20 public health interventions are not sufficient, but malaria is also forecasted to decrease in some of the higher
21 risk areas (Peterson, 2009; Caminade et al., 2014; Ryan et al., 2015; Khormi and Kumar, 2016; Leedale et
22 al., 2016; Murdock et al., 2016; Endo and Eltahir, 2020; Mordecai et al., 2020).
23
24 While malaria risk is predicted to decrease in some lowland tropical areas as temperatures become too hot
25 for vector or parasite development, other, warmer-adapted diseases like dengue and Zika, transmitted by
26 Aedes aegypti, are predicted to increase (Ryan et al., 2019; Ryan et al., 2021). In more temperate regions,
27 arboviruses and other vector-borne diseases with wider thermal breadths, such as West Nile fever, Ross
28 River fever, and Lyme disease, are predicted to increase with climate warming (Ogden et al., 2008; Leighton
29 et al., 2012; Shocket et al., 2018; Shocket et al., 2020; Couper et al., 2021), and drought can exacerbate these
30 effects of temperature (Paull et al., 2017).
31
32 A global analysis of 7346 wildlife populations and 2021 host-parasite combinations found that organisms
33 adapted to cool and mild climates are likely to experience increased risks of outbreaks with climate warming
34 while warm-adapted organisms may experience lower disease risk, providing further support for predictions
35 that climate change will increase infectious disease transmission in higher latitude regions across a
36 taxonomically diverse array of pathogens (robust evidence, high confidence) (Cohen et al., 2020). A study
37 examining the future risk of arboviruses (chikungunya, dengue, yellow fever, and Zika viruses) spread by
38 Aedes aegypti and Ae. albopictus projected increased disease risk due to interactions of multiple variables,
39 including increased human connectivity, urbanisation and climate change (Kraemer et al., 2019), although
40 vector species’ ranges broaden only slightly (Campbell et al., 2015).
41
42 In sum, climate change is expected to expand and redistribute the burden of vector-borne and other
43 environmentally-transmitted diseases by shifting many regions toward the thermal optima of vector-borne
44 disease transmission for multiple parasites, increasing transmission, while pushing temperatures above
45 optima and toward upper thermal limits for other vectors and pathogens, decreasing transmission (Mordecai
46 et al., 2019; Mordecai et al., 2020). These effects are mediated by other human impacts such as land use
47 change, mobility, socio-economic conditions, and vector and pathogen control measures (Parham et al.,
48 2015; Tjaden et al., 2018).
49
50 2.5.2 Projected Changes at Level of Biomes and Whole Ecosystems
51
52 2.5.2.1 Global Overview, Assessment of Ecosystem-level Models, and Sources of Uncertainties
53
54 Shifts in terrestrial biome and changes in ecosystem processes in response to climate change are most
55 frequently projected with dynamic global vegetation models (DGVMs), or land-surface models that form
56 part of Earth System Models, which use gridded climate variables, atmospheric CO2 concentration and
57 information on soil properties as input variables. Since AR5, most of DGVMs have been upgraded to capture
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1 carbon-nitrogen cycle interactions (e.g. Le Quéré et al., 2018), many also include a representation of
2 wildfire, and fire-vegetation interactions (Rabin et al., 2017), and a small number now also accounts for land
3 management (such as wood removal from forests, crop fertilisation harvest of irrigation (Arneth et al., 2017).
4 Other forms of disturbance, such as tree mortality in response to, for example, episodic weather extremes or
5 insect pest outbreaks, are relatively poorly represented, or not at all, although they demonstrably impact
6 calculated carbon cycling (Pugh et al., 2019a). Simulated biome shifts are generally in agreement in
7 projecting broad patterns at the global scales, but vary greatly regarding the simulated trends in historical and
8 future carbon uptake or losses, both regionally and globally (WGI, Chapter 5 AR6; Chang et al., 2017).
9
10 Similar to other models, models to project large-scale changes in vegetation and ecosystem processes have to
11 deal with structural uncertainty (associated with the choice and the representation of processes in models),
12 input-data uncertainty (associated with variability in initial conditions and parameter values) and error
13 propagation (associated with coupling models) (Rounsevell et al., 2019). The IPBES methodological
14 assessment report on scenarios and models of biodiversity and ecosystem services provides a comprehensive
15 overview over the relevant issues (Ferrier et al., 2016).
16
17 In order to assess the model’s performance, most models have been individually evaluated against a range of
18 observations. Moreover, in the annual updates of the global carbon budgets a model has to meet a small set
19 of basis criteria to have its output included (Le Quéré et al., 2018). More systematic benchmarking
20 approaches have also been proposed that utilise a range of different data sets (Kelley et al., 2013; Chang et
21 al., 2017), in order to assess multiple simulated processes. These methods in principle allow to assign quality
22 scores to models based on their overall performance (Kelley et al., 2013). So far, this scoring does not yet
23 allow a clear quality ranking of models since the individual DGVMs tend to score well for some variables
24 and badly for others. A recent comparison of global fire-vegetation model outputs was also able to clearly
25 identify outliers when using a formalised benchmarking and scoring approach (Hantson et al., 2020).
26 However, benchmarking does not address sources of uncertainty and it would be advisable to perform
27 “perturbed-physics” experiments, in which multiple model parameters are varied in parallel more frequently,
28 as a means to test parameter-value uncertainty (Wramneby et al., 2008; Booth et al., 2012; Lienert and Joos,
29 2018).
30
31 Species diversity impact ecosystem functioning and hence ecosystem services (Hooper et al., 2012; Mokany
32 et al., 2016). So far, however, integrated modelling of ecosystem processes and biodiversity across multiple
33 trophic levels and food webs is in its infancy (Harfoot et al., 2014). Whether or not enhanced integration of
34 state, function, and functional diversity across multiple trophic levels in models will markedly alter
35 projections of how ecosystems respond to climate change thus remains an open research question.
36
37 Beyond simulating dynamically biome shifts and carbon cycling, which are important aspects of climate
38 regulation, DGVMs can also provide information on a number of variables closely linked to other ecosystem
39 services, such as water availability, air quality or food provisioning (Krause et al., 2017; Rabin et al., 2020).
40 However, they are not intended to provide a comprehensive assessment of ecosystem services. For these,
41 other approaches applied, but to date these are mostly applied on regional scales and are only weakly
42 dynamic (Ferrier et al., 2016).
43
44 2.5.2.2 Projected Changes Globally at the Biome Level
45
46 Climate change and the associated change in atmospheric CO2 levels already exacerbate other human-caused
47 impacts on structure and composition of land and freshwater ecosystems, such as land-use change, nitrogen
48 deposition, of pollution. The relative importance of these drivers for ecosystems over the coming decades
49 will likely differ between biomes, but climate change and atmospheric CO2 will be pervasive unless we
50 manage to rapidly limit fossil-fuel emissions and warming (high confidence) (Pereira et al., 2010; Warren,
51 2011; Ostberg et al., 2013; Davies-Barnard et al., 2015; Pecl et al., 2017; Ostberg et al., 2018). Global
52 vegetation and Earth system models agree on climate-change driven shifts of biome boundaries potentially
53 of hundreds of km over this century, combined with several substantial alterations that take place within
54 biomes (e.g., changes in phenology, canopy structure and functional diversity, etc.). Large discrepancies
55 exist between models and between scenarios regarding the region and the speed of change (Gonzalez et al.,
56 2010b; Pereira et al., 2010; Pecl et al., 2017), but robust understanding is emerging in that the degree of
57 impact increases in high emission and warming scenarios (high confidence) (Figure 2.9).
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1
2
3
4 Figure 2.9: Projected fraction of global terrestrial area that could experience a biome shift by 2100, due to climate
5 change (filled symbols) or a combination of climate change and land use change (outline symbols), from publications in
6 Supplementary Table 2.S.3 (Projected vulnerabilities and risks of ecosystems to biome shifts). Circle filled (Bergengren
7 et al., 2011), square filled (Alo and Wang, 2008), diamond filled (Gonzalez et al., 2010b), triangle up filled (Scholze et
8 al., 2006), triangle down filled (Sitch et al., 2008), triangle on side filled (Li et al., 2018), cross filled (Warszawski et
9 al., 2013), circle outline (Ostberg et al., 2018), diamond outline (Eigenbrod et al., 2015).
10
11
12 Substantial changes in vegetation structure and ecosystem processes are already happening (see section 2.4).
13 Many of these observations have already been projected to take place as early as at least IPCC AR3
14 (Rosenzweig et al., 2007), and can they now be increasingly tested for their robustness with observational
15 evidence. These multiple changes in response to warming (and changes in precipitation and increasing
16 atmospheric CO2 levels that go hand-in-hand with the warming) are further expected for already relatively
17 small additional temperature increases, in particular in cold (boreal, tundra) regions, as well as in dry regions
18 (high confidence): alterations of 2‒47% of the areal extent of terrestrial ecosystems in scenarios of <2°C
19 warming above pre-industrial have been projected, increasing drastically with higher-warming scenarios
20 (Warren, 2011; Wårlind et al., 2014). More recent work, applying also probabilistic methods confirm the risk
21 of drastic changes in vegetation cover (e.g. forest to non-forest or vice versa) at the end of the 21st century
22 even for ca. 2°C warming scenarios, especially in tundra, and in tropical forest and savannah regions, with
23 more subtle changes (within a given biome types) likely to occur in all regions (Ostberg et al., 2013; Ostberg
24 et al., 2018). Model studies have found 5‒20% of terrestrial ecosystems affected by warming ca. 2‒3oC,
25 increasing to above one-third at a warming of 4‒5oC (Ostberg et al., 2013; Warszawski et al., 2013).
26
27 In general, vegetation types are projected to be moving into their 'neighbouring' climates, depending on
28 whether temperature or precipitation is expected to be the predominant factor, and how vegetation interacts
29 with the increasing CO2 levels in the atmosphere (Wårlind et al., 2014; Scheiter et al., 2015; Schimel et al.,
30 2015; Huntzinger et al., 2017). For instance, boreal or temperate forest vegetation is simulated to migrate
31 polewards, closed tropical (moist) forest is expected to transition towards dry tropical forest types, while
32 climate-driven degradation might expand arid vegetation cover (Sections 2.5.4.2.1-2.5.4.2.5) However,
33 'novel ecosystems', that is, communities with no current or historical equivalent because of the novel
34 combinations of abiotic conditions under climate change, are expected to be increasingly common in the
35 future (medium confidence) although the regions where these novel ecosystems might emerge are still
36 disputed (Reu et al., 2014; Radeloff et al., 2015; Ordonez et al., 2016). The possibility of these novel
37 ecosystems and the communities that live within them poses challenges to current modelling of ecosystem
38 shifts, and will require new approaches to conservation that are designed to adapt to rapid changes in species
39 composition and ensuing conservation challenges.
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1
2 2.5.2.3 Risk to Arid Regions
3
4 Shifts in arid system structure and functioning that have been observed to date (section 2.4.3.3) are projected
5 to continue and include widespread woody plant encroachment, notably in savanna systems in Africa,
6 Australia and South America, and attributed to interacting land use change, climate change, and CO2
7 fertilisation effects (Fensholt et al., 2012a; Fang et al., 2017; Stevens et al., 2017). Arid Mongolian Steppe
8 grassland did not respond to experimentally elevated CO2 (Song et al., 2019). Woody encroachment is
9 projected to continue or not reverse in North American drylands (Caracciolo et al., 2016), and in southern
10 African arid ecosystems (Moncrieff et al., 2014b). Dryland woody encroachment may increase carbon
11 stocks, depending on emissions scenario (Martens et al., 2021), but reduce soil water and biodiversity of
12 grassland-dependent species diversity (Archer et al., 2017). Warm season (C4) grass expansion into arid
13 shrublands risks sudden ecosystem transformation due to introduced wildfire (Bradley et al., 2016), a risk
14 anticipated for grass-invaded desert ecosystems of Australia and south-western United States (Horn and St.
15 Clair, 2017). Novel fire regimes in grassy shrublands have enhanced grass cover locally in southern African
16 Nama-Karoo (du Toit et al., 2015).
17
18 Range retractions are projected for endemic plants in southern Africa (Young et al., 2016) and dry
19 woodlands in Morocco (Alba-Sánchez et al., 2015). Increasing thermal stress is projected to increase woody
20 plant mortality in Sonoran Desert ecosystems (Munson et al., 2016), and facilitate perennial grass
21 replacement by xeric shrubs in the south-western USA (Bestelmeyer et al., 2018). Ecological effects may
22 occur rapidly when extreme events compound long-term trends (Hoover et al., 2015), but evolve more
23 slowly as opportunity costs accumulate due to warming (Cross-Chapter Paper 3; Cunningham et al., 2021).
24
25 2.5.2.4 Risk to Mediterranean-type Systems
26
27 All Mediterranean Type Ecosystems (MTEs) show high confidence in projected increases in the intensity
28 and frequency of hot extremes and decreases in the intensity and frequency of cold extremes and medium
29 confidence in increasing ecological drought due to increased evapotranspiration (all regions) and reduced
30 rainfall (excluding California, where model agreement is low) (see Chapter 11 of WG1). Projections also
31 show a robust increase in the intensity and frequency of heavy precipitation in the event of 2° C warming or
32 more for MTEs in South Africa, the Mediterranean Basin and California, but are less clear for Australia and
33 Chile (see Chapter 11 of WG1).
34
35 MTEs are characterised by the distinctive seasonal timing of precipitation and temperature and disruption of
36 this regime is likely to be critical for their maintenance. Unfortunately, projections of changes in rainfall
37 seasonality have received less attention and are far more uncertain than many other aspects of climate
38 change (Pascale et al., 2016; Breinl et al., 2020), limiting our ability to predict the ecological consequences
39 of climate change in MTEs. Responses to experimental manipulation of rainfall seasonality show potential
40 for shifts in plant functional composition and diversity loss, but results vary with soil type (van Blerk et al.,
41 2021).
42
43 Unfortunately, global and regional scale dynamic vegetation models show poor performance for large areas
44 of MTEs, because they do not characterise shrub and CAM-photosynthetic plant functional types well
45 (Moncrieff et al., 2015). Furthermore, the grain of these models are too coarse for quantifying impacts to
46 many vegetation formations which are patchy or of limited extent (e.g. forests). There is high confidence that
47 observations of high mortality in trees and other growth forms, reduced reproductive and recruitment
48 success, range shifts, community shifts towards more thermophilic species, and type conversions are set to
49 continue, either due to direct climate impacts through drought and other extreme weather events, or their
50 interaction with factors like fire and pathogens (Sections 2.4.3.6; 2.4.4.2; 2.4.4.3; 2.5.4.2.4; 2.5.5.2).
51
52 Fire is a key driver across most MTEs due to summer-dry conditions. Climate projections for the MTEs
53 translate into high confidence that periods of low fuel moisture will become more severe and prolonged and
54 that episodes of extreme fire weather will become more frequent and severe (see Chapter 11, Section 8.3
55 WGI). This will lead to the birth of novel fire regimes in MTEs characterised by an increase in the
56 probability of greater burned area and extreme wildfire events (e.g. megafires), with associated loss of
57 human life and property, and long-term impacts on ecosystems and accelerating the possible loss of
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1 resilience and capacity to recover (Abatzoglou and Williams, 2016; González et al., 2018; Boer et al., 2020;
2 Moreira et al., 2020; Nolan et al., 2020; Duane et al., 2021; Gallagher et al., 2021).
3
4 Fire is virtually certain to have additional impacts through compound events (AR6 WGI Chapter 11.8).
5 Extreme postfire weather is extremely likely to continue to impact diversity (Slingsby et al., 2017), retard
6 vegetation regrowth (Slingsby et al., 2020a) and accelerate vegetation shifts (Batllori et al., 2019). Any
7 increases in the intensity and frequency of heavy precipitation are highly likely to compromise soil stability
8 in recently burnt areas (Morán-Ordóñez et al., 2020). The impacts of fire often depend on interactions with
9 non-climatic factors such as habitat fragmentation (Slingsby et al., 2020b), management (Steel et al., 2015)
10 or the spread of flammable exotic plantation forestry and invasive species (Kraaij et al., 2018; McWethy et
11 al., 2018). Managing these factors provides opportunities for adaptation and mitigation (Moreira et al.,
12 2020).
13
14 Human adaptation and mitigation responses to climate change may create additional threats to MTEs. MTEs
15 have dry summers by definition, posing a challenge for the year-round supply of water to growing human
16 populations and agriculture. With recent major droughts in all MTEs (Section 2.4.3.6), there is increasing
17 reliance on groundwater for bulk water supply (Kaiser and Macleod, 2018). The majority of groundwater
18 systems have exceeded or are rapidly approaching their environmental flow limits (de Graaf et al., 2019),
19 threatening human populations and ecosystems that depend on these systems for their persistence through
20 unfavourable climatic conditions (McLaughlin et al., 2017 Plants). Similarly, much of the MTEs are open
21 shrublands and grasslands and proposed extensive tree-planting to sequester atmospheric CO2 could result in
22 loss of biodiversity and threaten water security (Doblas-Miranda et al., 2017; Bond et al., 2019).
23
24 2.5.2.5 Risk to Grasslands and Savannas
25
26 Worldwide, woody cover is increasing in savannas (Buitenwerf et al., 2012; Donohue et al., 2013; Stevens et
27 al., 2017), as a result interactions of elevated CO2 combined with altered fire and herbivory impacts (i.e.
28 from land-use change; see Section 2.4.3.5; CCP3.2; Venter et al., 2018; Wu et al., 2021). In some regions,
29 altered climate may also contribute (CCP3.2). Elevated CO2 benefits plants with C3 photosynthesis (often
30 woody plants), more than C4 species (Moncrieff et al., 2014a; Scheiter et al., 2015; Knorr et al., 2016a).
31 Increases in woody vegetation in grassy ecosystems could provide some carbon increase (medium
32 confidence) (Zhou et al., 2017; Mureva et al., 2018), but is expected to decrease biodiversity (Smit and Prins,
33 2015; Abreu et al., 2017; Andersen and Steidl, 2019), decrease water availability (Honda and Durigan, 2016;
34 Stafford et al., 2017) and alter ecosystem services like grazing and wood provision (high confidence)
35 (Anadon et al., 2014).
36
37 The relative importance of climate, disturbance (e.g. fire/herbivory) and plant feedbacks in shaping present
38 and future savanna distribution vary between continents (Lehmann et al., 2014), which makes projections of
39 changing biome extent challenging (Moncrieff et al., 2016). It has been shown that simulation studies that do
40 not account of CO2 interactions and only consider climate change impacts do not realistically capture the
41 future distribution of savannas (high confidence) (Higgins and Scheiter, 2012; Moncrieff et al., 2016;
42 Scheiter et al., 2020). Due to the continued strong effect of CO2 on tree (and shrub) to grass ratios in future,
43 models suggest a loss of savanna extent and conversion into closed canopy forest/thicket and an expansion
44 of savanna-type vegetation into arid grasslands (Wårlind et al., 2014; Moncrieff et al., 2016). In arid
45 savannas and their interface to grasslands, survival of woody vegetation (which may be stimulated to grow
46 by increasing CO2) will depend l on their capacity to survive potentially more severe and frequent droughts
47 (Sankaran and Staver, 2019). Across a range of models, for RCP4.5 future climate change and CO2
48 concentrations, savanna expanse declined by around 50% (converting to closed canopy systems) by 2070 in
49 Africa and South America, 25% in Asia with small changes in Australia (Moncrieff et al., 2016; Kumar et
50 al., 2021). Future fire spread is expected to be reduced with increased woody-dominance (Scheiter et al.,
51 2015; Knorr et al., 2016b; Scheiter et al., 2020), feeding back to further increase tree to grass ratios (high
52 confidence).
53
54 Like the tropical forest biome, savannas are at large risk, given the projected climate changes in combination
55 with land-use change (see Cross Chapter Paper 3). About 50% of Brazilian Cerrado has been transformed to
56 agriculture and pastures (Lehman and Parr, 2016), and African savannas have been proposed to follow a
57 similar tropical agricultural revolution pathway in order to enhance agronomical prosperity (Ryan et al.,
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1 2016). In fact, indirect climate change impacts arising from mitigation efforts on land may be particularly
2 perilous to savannas: extensive tree-planting to restore ecosystems and remove CO2 from the atmosphere, as
3 pledged, for example, under the African Forest Restoration Initiative, could lead to carbon losses, loss of
4 biodiversity and damage ecosystem’s water balance if trees are planted in what naturally are grasslands or
5 savannas (Box 2.2, FAQ2.6; Bond et al., 2019).
6
7 2.5.2.6 Risk to Tropical Forest
8
9 Key factors affecting the future distribution of tropical humid and dry forests are amounts and seasonalities
10 of precipitation, increased temperatures, prolonged droughts and droughted-moderated fires (robust
11 evidence, high agreement) (Bonai et al., 2016; Corlett, 2016; Lyra et al., 2017; Anderson et al., 2018; da
12 Silva et al., 2018; Fontes et al., 2018; O'Connell et al., 2018; Aguirre-Gutiérrez et al., 2019; Bartlett et al.,
13 2019; Brando et al., 2019; Stan and Sanchez-Azofeifa, 2019). Probability of severe drought is projected to
14 quadruple in natural areas in Brazil with above 2°C warming (Barbosa and Lakshmi Kumar, 2016; Marengo
15 et al., 2020). Most multi-model studies assuming rapid economic growth/business-as-usual scenarios (A2,
16 A1B, RCP8.5) show an increase in future woody biomass and areas of woody cover towards the end of the
17 21st century in the temperate regions (Boit et al., 2016; Nabuurs et al., 2017) and in tropical forests in East
18 Africa (Ross et al., 2021) but decrease in the remaining tropical regions (Anadón et al., 2014; Boit et al.,
19 2016; Lyra et al., 2017; Nabuurs et al., 2017; Maia et al., 2020). Terrestrial species are predicted to shift to
20 cooler temperatures and higher elevations (Pecl et al., 2017). Tropical species are more susceptible to
21 climate warming than temperate species (Rehm and Feeley, 2016; Sentinella et al., 2020). This susceptibility
22 will be exacerbated by road-building increasing ease of access into forests (Brinck et al., 2017; Taubert et al.,
23 2018; Bovendorp et al., 2019; Senior et al., 2019). Furthermore, most tropical cloud forest species are unable
24 to invade grasslands and this will increase risk of extinctions in tropical cloud forests (Rehm and Feeley,
25 2015).
26
27 Sea level rise as the result of climate change is likely to influence mangroves in all regions, with greater
28 impact on North and Central America, Asia, Australia, and East Africa than West Africa and South America
29 (robust evidence, high agreement) (Alongi, 2015; Ward et al., 2016). On a small scale, mangroves are
30 potentially moving landward (Di Nitto et al., 2014), while on a large scale they will continue to expand
31 poleward (Alongi, 2015).
32
33 Most simulations predict a significant geographical shifts of transition areas between tropical forests and
34 savanna in the tropical and subtropical Americas and Himalayas (Anadón et al., 2014) (Rashid et al., 2015).
35 Forest die-back, as postulated for the Amazon region, does not occur in the majority of simulations (Malhi et
36 al., 2009; Poulter et al., 2010; Rammig et al., 2010; Higgins and Scheiter, 2012; Huntingford et al., 2013;
37 Davies-Barnard et al., 2015; Sakschewski et al., 2016; Wu et al., 2016a). Model projections of future
38 biodiversity in tropical forests are rare. Arguably, species are most vulnerable to climate change effects in
39 higher altitudes or at the dry end of tropical forest occurrence (medium evidence, medium agreement)
40 (Krupnick, 2013; Nobre et al., 2016; Trisurat, 2018). Tropical lowlands are expected to lose plant species as
41 temperatures rise above species’ heat tolerance but could also generate novel communities of heat tolerant
42 species (robust evidence, high agreement) (Colwell et al., 2008; Trisurat et al., 2009; Trisurat et al., 2011;
43 Krupnick, 2013; Zomer et al., 2014a; Zomer et al., 2014b; Sullivan et al., 2020; Pomoim et al., 2021).
44
45 Statistical models that correlate data on species abundance with information on human pressures (such as
46 land-use change (Srichaichana et al., 2019), population density (Leclère et al., 2020) hunting (Mockrin et al.,
47 2011) found for tropical and sub-tropical forests that birds, invertebrates, mammals and reptiles show a
48 decline in their probability of presence with declining forest cover, which is particularly pronounced in forest
49 specialists or narrow-ranged species (Newbold et al., 2014). Different soil fauna groups showed different
50 responses in abundance and diversity to climate change conditions (Coyle et al., 2017; Facey et al., 2017) but
51 these changes can impact decomposition rates and biogeochemical cycles (medium evidence, low
52 agreement).
53
54 Invasive plant species are predicted to expand upward by 500-1,500m in the Western Himalaya (Thapa et al.,
55 2018), and by 6-35% per year from the current extent in South America (robust evidence, high agreement)
56 (Bhattarai and Cronin, 2014). Global assessment (Wang et al., 2017) also revealed that ecoregions of high
57 elevation tropical forests and sub-tropical coniferous forests have high risk of invasive plant expansion in the
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1 low CO2 emission scenarios, with negative impacts on ecosystem functioning and local livelihoods (Shrestha
2 et al., 2019).
3
4 The impact of unsustainable land use on tropical forests continues in all regions (see Cross-Chapter Paper 7).
5 Projected climate changes will not only cause impacts on biodiversity but also on the livelihoods of affected
6 people (robust evidence, high agreement). Increased drought drives crop failures that cause local
7 communities to expand agricultural area by further clearing native forests (Desbureaux and Damania, 2018).
8 Climate change is projected to enlarge the area of suitability for booming tree crops such as oil palm, acacia,
9 Eucalyptus, and rubber (Koninck et al., 2011; Cramb et al., 2015; Nath, 2016; Hurni et al., 2017; Li et al.,
10 2017; Varkkey et al., 2018). An increase of 8% in area of rubber plantations in Yunnan province, China,
11 between 2002–2010 to higher altitude due to decreased environmental limits, potentially increases pressure
12 on remaining biodiversity both within and outside of protected areas (Zomer et al., 2014a). As a
13 consequence, the suitable area for mammals is projected to be reduced by 47.7% (RCP 2.6) and 67.7%
14 (RCP8.5) by 2070, with large variability depending on the different species (See also Cross-Chapter Paper 7;
15 Brodie, 2016).
16
17 2.5.2.7 Risk to Boreal and Temperate Forests
18
19 As in the Arctic, warming substantially exceeding the global average has already been observed for the
20 northern parts of the temperate and boreal forest zone (Gauthier et al., 2015), and is projected to continue
21 (see Chapter 4 of WGI, see Cross-Chapter Paper 6). As a consequence, boreal tree species are expected to
22 move northwards (or in mountain regions: upwards) into regions dominated by tundra, unless constrained by
23 edaphic features, and temperate species are projected to grow in regions currently occupied by southern
24 boreal forest (high confidence). In both biomes, deciduous trees are simulated to increasingly grow in
25 regions currently dominated by conifers (Wårlind et al., 2014; Boulanger et al., 2017). These simulation
26 results have been supported by observational examples. In Eastern Siberia, fire disturbance of larch-
27 dominated forest was followed by recovery to birch-dominated forest (Stuenzi and Schaepman-Strub, 2020).
28 In Alberta Lodgepole Pine (Pinus contorta) lost its dominant status after attacks by Mountain Pine Beetles
29 (Dendroctonus ponderosae) caused the canopy to switch to non-pine conifers and broadleaved trees
30 (Axelson et al., 2018). In contrast to the examples above, some boreal forests have proven resilient to
31 disturbances, including to recent unprecedented spates of insect attacks (Campbell et al., 2019a; Prendin et
32 al., 2020).
33
34 Reforestation, either natural or anthropogenic, leads to summer cooling and winter warming of the ground,
35 while forest thinning or removal by fire has the reverse effects and deepens the upper layer free of
36 permafrost (Stuenzi et al., 2021a). Interactions between permafrost and vegetation are important. For
37 example, trees in East Siberian taiga obtained water mostly from rain in wet summers and mostly from
38 permafrost meltwater in dry summers (Sugimoto et al., 2002), suggesting that these forests will be
39 particularly vulnerable to combination of drought with retraction further underground of permafrost under
40 climate warming.
41
42 2.5.2.8 Risk to Peatland Systems
43
44 The overall effect of climate change on the extent of northern peatlands is still debated (limited evidence, low
45 agreement). It is expected that climate change will drive high-latitude peatland expansion poleward of their
46 present distribution due to warming, permafrost degradation, and glacier retreat, which could provide new
47 land and conditions favourable for peat development (limited evidence, medium agreement) (Zhang et al.,
48 2017b), as seen during the last deglacial warming (robust evidence, high agreement) (MacDonald et al.,
49 2006; Jones and Yu, 2010; Ratcliffe et al., 2018). Peatland area loss (shrinking) near the southern limit of
50 their current distribution or in areas where the climate becomes unsuitable is also expected (medium
51 evidence, medium agreement) (Section 2.3.4.3.2; Finkelstein and Cowling, 2011 temperature, and; Gallego-
52 Sala and Colin Prentice, 2013; Schneider et al., 2016; Müller and Joos, 2020), though these peatlands could
53 persist if moisture is maintained via peatlands' self-regulating capacity. In Western Canada, a study suggests
54 that peatlands may persist until 2100, even though the climate will be less suitable (Schneider et al., 2016).
55 Simulations suggest that climate change driven increases in temperature and atmospheric CO2 could drive
56 reductions in the northern peatland area up to 18% (SSP1-2.6), 41% (SSP2-4.5), and 61% (SSP5-8.5) by
57 2300 (Müller and Joos, 2020). This is in contrast with findings of northern peatland persistence and
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1 expansion under RCP2.6 and RCP6.0 scenarios during 1861–2099 by another modelling study (Qiu et al.,
2 2020). In the tropics, the only available study suggests peatland area will increase until 2300, mainly due to
3 the CO2 fertilisation effect (Müller and Joos, 2020).
4
5 The combination of climate and land-use change represents a substantial risk to peatland carbon stocks, but
6 full assessment is impeded because peatlands are yet to be included in Earth System models (limited
7 evidence, high agreement) (Loisel et al., 2021). It is expected that the carbon balance of peatlands globally
8 will switch from sink to source in the near future (2020-2100), mainly because tropical peatland emissions,
9 together with those from climate change -driven permafrost thaw, will likely surpass the carbon gain
10 expected from climate change-driven enhanced plant productivity in the northern high latitudes (Gallego-
11 Sala et al., 2018; Chaudhary et al., 2020; Turetsky et al., 2020; Loisel et al., 2021), mainly because of
12 groundwater drawdown (robust evidence, medium agreement) (Hirano et al., 2014; Brouns et al., 2015; Cobb
13 et al., 2017; Itoh et al., 2017; Evans et al., 2021). The overall northern peatland carbon sink has been
14 simulated to persist for at least 300 years under RCP2.6, but not under RCP8.5 (Qiu et al., 2020).
15
16 Increases in fire extent, severity, and duration are expected in all peatland regions in the future due to
17 temperature increases (Section 4.3.1.1), changes in precipitation patterns (section 4.3.1.2), and increases in
18 ignition sources (such as lightning) (Section 5.4.3.2), with associated rapid carbon losses to the atmosphere
19 (medium evidence, high agreement) (Dadap et al., 2019; Chen et al., 2021a; Nelson et al., 2021). For
20 example, drought has been linked to fires in SE Asian peatlands (Field et al., 2009) and there are predicted
21 decreases in mean summer precipitation (10-30%) for high and low RCPs, particularly over the Indonesian
22 region by mid and late twenty-first century (Section 12.4.2.2; Tangang et al., 2020; Taufik et al., 2020).
23 During wet years, the fire probability in Indonesian peatlands also significantly increases (+15-40 %) when
24 July-October temperatures surpass 0.5°C anomalies compared to a 1995-2015 baseline (Fernandes et al.,
25 2017). Overall, current evidence suggests that peat carbon losses via fire have the potential to be equal to, or
26 greater than, losses due to human peatland drainage and disturbance (limited evidence, high agreement)
27 (Turetsky et al., 2015).
28
29 In permafrost peatlands, studies differ, with some projecting net loss or and others net gain of carbon
30 (medium evidence, low agreement) (Estop-Aragonés et al., 2018; Hugelius et al., 2020; Loisel et al., 2021;
31 Väliranta et al., 2021). In some permafrost peatlands, prolonged and warmer growing seasons due to climate
32 change (section 2.3.4.3.1), along with increases in nitrogen deposition since 1850 (Lamarque et al., 2013),
33 are promoting plant primary productivity. Other studies indicate increased nitrogen-mediated sequestration
34 could be exceeded by increased decomposition due to climate-change-driven warming and fire (medium
35 evidence, low agreement) (Natali et al., 2012; Vonk et al., 2015; Keuper et al., 2017; Burd et al., 2018;
36 Estop-Aragonés et al., 2018; Gallego-Sala et al., 2018; Serikova et al., 2018; Wild et al., 2019; Chaudhary et
37 al., 2020; Hugelius et al., 2020).
38
39 Any climate change or human-driven degradation of peatlands will also entail losses in water storage
40 (limited evidence, high agreement) (Wooster et al., 2012 drought and; Hirano et al., 2015; Cole et al., 2019;
41 Taufik et al., 2019) and biodiversity (Harrison, 2013; Lampela et al., 2017; Renou-Wilson et al., 2019). The
42 environmental archive contained in peat that preserves records of vegetation, hydrology, climate change,
43 pollution and/or human disturbances is also lost as the peatlands degrade (Greiser and Joosten, 2018).
44 (Kasischke and Turetsky, 2006; MacDonald et al., 2006; Turunen, 2008; Field et al., 2009; Flannigan et al.,
45 2009; Jones and Yu, 2010; Kasischke et al., 2010; Peterson et al., 2010; Finkelstein and Cowling, 2011
46 temperature, and; Rooney et al., 2012; Gallego-Sala and Colin Prentice, 2013; Lamarque et al., 2013; Hirano
47 et al., 2014; Brouns et al., 2015; Turetsky et al., 2015; Miettinen et al., 2016; Schneider et al., 2016; Cobb et
48 al., 2017; Fernandes et al., 2017; Itoh et al., 2017; Gallego-Sala et al., 2018; Greiser and Joosten, 2018;
49 Ratcliffe et al., 2018; Dadap et al., 2019; Leifeld et al., 2019; Chaudhary et al., 2020; Hoyt et al., 2020;
50 Müller and Joos, 2020; Qiu et al., 2020; Tangang et al., 2020; Taufik et al., 2020; Turetsky et al., 2020; Chen
51 et al., 2021a; Evans et al., 2021; Loisel et al., 2021; Nelson et al., 2021; Qiu et al., 2021)
52
53 2.5.2.9 Risk to Polar Tundra Ecosystems
54
55 For boreal-tundra systems, AR5 projected transformation of species composition, land cover and permafrost
56 extent, decreasing albedo and increasing greenhouse gases emission (medium confidence). The Special
57 Report on Global Warming of 1.5°C classified tundra and boreal forests as particularly vulnerable to
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1 degradation and encroachment of woody shrubs (high confidence). The Special Report on Oceans and
2 Cryosphere (SROCC) projected climate-related changes to arctic hydrology, wildfire and abrupt thaw, (high
3 confidence) and broad disappearance of arctic near-surface permafrost in this century, with important
4 consequences for global climate (very high confidence). Chapter 2 of AR6 has focused on new key findings
5 about observed and projected changes in tundra vegetation and related hydrology, with implications for
6 feedbacks to the climate system.
7
8 Due to the rapid warming in high northern latitudes, Arctic tundra is one of the terrestrial biomes where
9 climate change impacts are already clearly visible (Settele et al., 2014; Uboni et al., 2016). Climate models
10 project that warming for the Arctic is likely to continue at more than double the global rate. Compared to the
11 period 1995-2014, mean annual surface air temperatures in Arctic tundra are projected to increase by 7.9°‒
12 10°C by the end of the century for scenarios of high greenhouse gas emissions (RCP 7.0 and 8.5). For
13 scenarios of low greenhouse gas emissions (RCP 1.9 and 2.6), the projected increase is 2.6°‒3.2°C (see
14 Chapter 4 pf WGI). The Arctic is also projected to have among the largest increases in precipitation globally,
15 although there is high uncertainty in these projections. In contrast to climate change, land use change is
16 projected to be very low in Arctic tundra systems (van Asselen and Verburg, 2013).
17
18 Models of vegetation response to climate project acceleration in coming decades of observed increases in
19 shrub dominance and boreal forest encroachment that have been driven by recent warming (Settele et al.,
20 2014), leading to a shrinking of the area of tundra globally (medium confidence) (Mod and Luoto, 2016;
21 Gang et al., 2017). Simulating changes in tundra vegetation is complicated by permafrost dynamics (e.g.
22 formation of thaw ponds), changes in precipitation, or low nutrient availability, which may promote
23 abundance of graminoids (van der Kolk et al., 2016). The changes in vegetation, when combined with
24 warming and increased precipitation effects on soil thawing and carbon cycling, are projected to modify
25 greenhouse gas emissions and have biophysical feedbacks to regional and global climate. Large uncertainty
26 in modelled carbon cycle changes arises from differences between the vegetation models (Nishina et al.,
27 2015; Ito et al., 2016). In addition, climate change is expected to strongly interact with other factors, such as
28 fire, to further increase uncertainty in projections of tundra ecosystem function (Jiang et al., 2017).
29
30 2.5.2.10 Committed Impacts of Climate Change on Terrestrial Ecosystems and Implications of Overshoot
31
32 Projections point to potentially large changes of canopy structure and composition within and across the
33 terrestrial biomes in response to climate change and changes in atmospheric CO2. These changes will
34 contribute to altered ecosystem carbon uptake and losses, biophysical climate feedbacks (Sections 2.3.2;
35 2.4.4; 2.5.3.2; 2.5.3.3. 2.5.3.4, 2.5.3.5, Figure 2.10, Table 2.4), and multiple other ecosystem services
36 (Sections 2.5.3, 2.5.4), as well as for biodiversity (Sections 2.4.2, 2.4.3, 2.4.4, 2.4.5, 2.5.1.3, 2.5.1.4, 2.5.2,
37 Figure Box 2.1.1, Table Box 2.1.1, Table SM2.4). Until now, most studies project changes over next decades
38 until the end of this century.
39
40 However, there is an increasing body of literature that has found continued, longer-term responses of
41 ecosystems to climate change, so-called 'committed changes,' that arise from lags that exist in many systems.
42 Many processes in ecosystems take more than a few decades to quasi-equilibrate to environmental changes.
43 Therefore, trends of changing vegetation cover identified in simulations of transient warming continue to
44 show up in simulations that hold climate change at low levels of warming (medium confidence) (Boulton et
45 al., 2017; Pugh et al., 2018; Scheiter et al., 2020). Such changes, which could tip ecosystems into an
46 alternative state, could also be triggered by a ‘warming overshoot’ – if global warming were to exceed a
47 certain threshold, even if mean temperatures afterwards decline again (Albrich et al., 2020a).
48
49 For instance, even if warming achieved by 2100 remained constant after 2100, such committed responses
50 continue to occur. These include: (1) continued Amazon forest loss (Boulton et al., 2017), consistent with
51 results in (Pugh et al., 2018) that found continued tropical forest cover loss across a range of models and
52 simulation set-ups, and (2) across Africa, an increased shift towards woody C3 vegetation was found in
53 equilibrium state, the overall response depending on the atmospheric CO2 concentration (Scheiter et al.,
54 2020). In Pugh et al. (2018), the opposite was found for boreal forest cover, which showed a strong
55 committed increase. The committed changes in vegetation composition correspond to large committed
56 changes in terrestrial carbon uptake and losses (Boulton et al., 2017; Pugh et al., 2018; Scheiter et al., 2020),
57 and would plausibly also appear in other ecosystem functioning and services. These studies point to the
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1 importance of having not only a multi-decadal but also a multi-century perspective when exploring the
2 impacts of political decisions on climate change mitigation taken now. Even if climate-warming targets are
3 met, published evidence so far suggests that fundamental changes in some ecosystems are likely as these
4 correspond to well-understood ecosystem physiological responses that trigger long-term changes in
5 composition.
6
7
8 [START BOX 2.1 HERE]
9
10 Box 2.1: Assessing Past Projections of Ecosystem Change Against Observations
11
12 To assess future climate change impacts on ecosystems we use models to project their future distribution.
13 Comparing the trends in the observed changes against the projections can help assess the strength of the
14 model projections. In this box, we compare observed trends of changes in ecosystem structure to projections
15 highlighted in previous IPCC reports (specifically AR3 (IPCC, 2001), AR4 (Fischlin et al., 2007) and AR5
16 (Settele et al., 2014). We use this to assess how well the projections are matching up with observed changes.
17 The map represents studies documenting observed changes in common plant functional groups (e.g. trees,
18 grasses, shrubs). Studies, documenting changes in plant functional groups, were collated from published
19 papers in natural and semi-natural areas. Studies were included if climate change, or interactions between
20 climate change and land use showed a causal link to the observed change. Studies were excluded if the
21 changes only from landscape/land use transformation (e.g. deforestation). In each paper, we recorded the
22 geographical location, the type of functional change and noted the causes. Observed changes are plotted onto
23 a biome map derived from the WWF ecoregions database (Olson et al., 2001). Trends in changing plant
24 functional types are good indicators of potential biome shifts and are used to assess how observations match
25 up with projections.
26
27
28
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1 Figure Box 2.1.1: Observed changes in the distribution of plant functional types that are caused by climate change or
2 combination of land use and climate change. Shifts in plant functional types are indicative of shift in biome function
3 and structure
4
5
6 Table Box 2.1.1: Comparison of projections on biome change from the Third, Fourth and Fifth assessment report
7 (IPCC, 2001; Fischlin et al., 2007; Settele et al., 2014) with observed changes in ecosystems as assessed in this current
8 report (see section 2.4 and Fig Box 2.1.1). Observed changes marked in bold show good agreement with past
9 projections, those in red show mismatch with observations and projections.
Biome AR3 AR4 AR5 Observed trends 1990-2021
Mediterranean Increased disturbance Loss of 65% of area due to Range contractions of Increased in water deficit and fire
Type ecosystems by fire and warming warming. Increased fire all species activity (sections 2.4.3.6, 2.4.4.2)
will cause a loss of frequencies will favor causing declines in diversity, tree
unique habitats resprouting plants. An mortality (Fig Box 2.1.1) with
increase in grass dominance. resprouting trees worst affected.
Forest expansion within
MTE’s systems due to Increasing dominance of grasses
elevated CO2. (often alien). Increasing
dominance of deciduous over
evergreen species (Fig Box 2.1.1).
Tundra Tree and shrub Increased woody plant growth Continued woody Increase in woody shrub cover in
encroachment into due to longer and warmer expansion in tundra tundra and expansion of boreal
tundra. growing seasons and regions with reduced forest into tundra. (Fig Box 2.1.1,
replacement of dwarf tundra surface albedo due to 2.4.3.4)
by shrub tundra less snow and more
woody cover
Poleward expansion of tundra
into polar desert and
encroachment of coniferous
trees into tundra
Boreal forest Reduced productivity Extensive boreal tree spread Expansion into Tundra and
due to weather related into tundra. upslope tree line advance
disturbances (e.g. (Section 2.4.3.8 and Fig Box
increase fire risk). Boreal forest dieback within 2.1.1).
boreal zone and contraction
Deciduous broadleaf of boreal forest at southern Increased mortality due to
tree encroachment ecotone with continental drought, fire, beetle infestations
into boreal forest grasslands (Sections 2.4.3.8, 2.4.42.1,
2.4.4.3.1)
Tropical forest Increasing CO2 Increases in forest Shift in the climate Expansion of tropical forest into
concentration would productivity and biomass envelope of moist savannas in Africa, Asia, South
increase net primary through increased CO2 with tropical forests but America (Section 2.4.3.7, Fig Box
productivity localised decreases in the forests are less likely to 2.1.1)
Amazon. Shift in forest undergo major
species composition. retractions or Forest biomass increases (though
Expansion of forest area into expansions than slowing). (Section 2.4.4.4)
mesic savanna. suggested in AR4
Forest degradation from drought,
warming x fire and shorter
residence time of trees. (Section
2.4.3.7)
Shift in species composition
towards species with more arid
adapted trait (Section 2.4.3.7)
Temperate forest Forest decline and Increase in tree mortality Map indicates a shift towards
increased mortality from drought related declines. deciduous species in W N America
(Fig Box 2.1.1)
A general increase of
deciduous at the expense of Tree death due to interactions
evergreen vegetation is with drought x pest outbreaks x
predicted at all latitudes fire (2.4.3.8, 2.4.42.1., 2.4.4.3.1)
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Grasslands and Increasing CO2 Increased tree dominance in Rising CO2 will Greening and encroachment
savannas concentration will savannas and grasslands increase the likelihood across tropical and temperate
increase net primary (from elevated CO2). With C3 woodier states (but the savannas in Africa, Asia,
productivity plants benefiting more than transition will vary in Australia and N America
C4 plants different (Section 2.4.3.5)
environments)
Expansion of trees into
grasslands and advancement of
tree lines
Signs of increased C4 grass
productivity in drought conditions.
Increased C3 grass productivity
(Section 2.4.3.5).
Desert/ arid An increase in desert Greening (increased LAI, woody
shrublands vegetation productivity was cover) and increased herbaceous
projected in southern Africa, production is occurring at desert
the Sahel, central Australia, grassland interfaces (Chapter –
the Arabian Peninsula and Cross-Chapter Paper 3).
parts of central Asia due to a
positive impact of rising
atmospheric CO2.
1
2
3 Assessment: There is a high agreement between observations and projections of tree death in temperate and
4 boreal forests, with current projections (AR6) indicating this trend will continue (Section 2.5.4). Forest death
5 is most widely recorded in central Europe and Westerns North America (Fig Box 2.1.1). There is also very
6 high agreement between observations and projections of woody encroachment in savannas, grasslands and
7 tundra, with projections also indicating that this trend is likely to continue (Section 2.5.4). Observations of
8 desert greening show good agreement with earlier projections. Patterns of desertification are also occurring
9 although the geographical match between projections and observations shows moderate agreement, likely
10 due to the strong role of land use in this process. Projections of tropical forest expansion into mesic savannas
11 and boreal forest expansion into tundra also shows agreement with observations.
12
13 Projections on the future of Mediterranean shrublands, deserts, xeric shrublands and temperate grassy
14 systems are limited making assessment of this relationship less clear. It is also unclear, due to limited
15 observations, how widespread a shift from deciduous forest species to evergreen forest species is. Some
16 observations suggest this is occurring although it is not clear how widespread this change is and if the
17 geographical pattern is as projected.
18
19 [END BOX 2.1 HERE]
20
21
22 2.5.3 Risk Assessment of Ecosystems and Related Services
23
24 2.5.3.1 Risks in Protected Areas
25
26 National parks and other protected areas, which, in June 2021, covered 15.7% of global terrestrial area
27 (UNEP-WCMC, 2021), conserve higher biodiversity than adjacent unprotected areas (Gray et al., 2016), and
28 protect one-fifth of global vegetation carbon stocks and one-tenth of global soil carbon stocks (Section
29 2.4.4.4). This section assesses climate change specifically in protected areas. Even though it is in a part of
30 the chapter on projected risks, this section includes both observed exposure and projected risks to gather the
31 chapter information on protected areas in one place.
32
33 2.5.3.1.1 Observed exposure of protected areas
34 Deforestation, agricultural expansion, overgrazing, and urbanisation exposed to intense human pressure one-
35 third of global protected area (6 million km2) in 2009, a 6% increase from 1993 (Venter et al., 2016; Jones et
36 al., 2018). The observed change in exposure to climate change has not yet been quantified for protected areas
37 globally but research has analysed spatial patterns and magnitudes of observed changes for the 360 000 km2
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1 system of U.S. national parks (Gonzalez et al., 2018), including the first national park in the world. From
2 1895 to 2010, mean annual temperature of the U.S. national park area increased at a rate of 1 ± 0.2ºC
3 century-1, double the rate of the U.S. as a whole, and precipitation decreased on 12% of the national park
4 area, compared with 4% for the U.S. as a whole, due to a high fraction of U.S. national park area in the
5 Arctic, at high elevations, and the arid Southwestern USA. (Gonzalez et al., 2018). In addition, analyses of
6 weather station measurements in and near six South African National Parks found that maximum
7 temperature increased at a rate of 0.024 ± 0.003ºC y-1 from 1960 to 2010 (Van Wilgen et al., 2016). While a
8 substantial fraction of global protected area has been exposed to observed human land cover change, the
9 global exposure to observed climate change is unquantified.
10
11 2.5.3.1.2 Projected risks in protected areas
12 Under a climate change scenario of ~3.5ºC temperature increase by 2070, current climate could disappear
13 from individual protected areas that comprise half of global protected area and novel climates (climate
14 conditions that are currently not present within an individual protected area) could expose half of global
15 protected area (Hoffmann et al., 2019b). A lower emissions scenario of ~1.5ºC could reduce the climate
16 disappearance to 40% and the exposure to novel climates to 41% (Hoffmann et al., 2019b). Models project
17 the highest projected exposure in subtropical projected areas (Hoffmann and Beierkuhnlein, 2020). Projected
18 disappearance of current climate conditions from protected areas is most extensive in Africa, Oceania, and
19 North and South America (Elsen et al., 2020).
20
21 Projections indicate higher exposure to novel climates of tropical rainforests, shrublands, and grasslands,
22 temperate conifer forests and grasslands, and tundra (Hoffmann et al., 2019b; Elsen et al., 2020). A climate
23 change scenario of ~3.5ºC temperature increase by 2100 could expose 32% of protected area in humid
24 tropical forests, 1.6 million km2 in 2000, to climate that would be novel to humid tropical forest protected
25 areas, while climate currently present in humid tropical forest protected areas could disappear from 0.6
26 million km2, 12% of current total area by 2050 (Tabor et al., 2018). High deforestation and climate change
27 combined could expose 2% of the humid tropical forest protected area (Tabor et al., 2018). Regional
28 analyses under RCP8.5 also project substantial disappearance of current climate from protected areas in
29 Bolivia, Chile, and Peru (Fuentes-Castillo et al., 2020), Canada, Mexico, and the U.S. (Batllori et al., 2017;
30 Holsinger et al., 2019), China (Zomer et al., 2015), Europe (Nila et al., 2019), and Indonesia (Scriven et al.,
31 2015). Projected climate change could expose an extensive part of global protected area to disappearing and
32 novel climate conditions (high confidence) (Cross-Chapter Paper 1).
33
34 Continued climate change increases risks to individual species and vegetation types in protected areas. Under
35 a climate change scenario of 4ºC temperature increase by 2100, suitable climate for two species of baobab
36 trees (Adansonia perrieri, A. suarezensis) in Madagascar could shift entirely out of the protected areas
37 network (Vieilledent et al., 2013). Other species and vegetation types at risk of partial disappearance of
38 suitable climate from protected areas include Atlantic Forest amphibians in Brazil (Lemes et al., 2014), birds
39 in Finland (Virkkala et al., 2013), birds and trees in Canada and Mexico (Stralberg et al., 2020), bog
40 woodlands in Germany (Steinacker et al., 2019), butterflies and mammals in Egypt (Leach et al., 2013), and
41 tropical dry forests in Mexico (Prieto-Torres et al., 2016). Projected disappearance of suitable climate
42 conditions from protected areas increase risks to the survival of species and vegetation types of conservation
43 concern in tropical, temperate, and boreal ecosystems (high confidence) (Cross-Chapter Paper 1).
44
45 Protected rivers, lakes, and other freshwater protected areas require inter-catchment connectivity to maintain
46 species and population movements (Bush et al., 2014a; Hermoso et al., 2016; Thieme et al., 2016), but dams
47 and other barriers interrupt connectivity (Grill et al., 2019). Climate change could also reduce freshwater
48 connectivity (Section 2.3.3.3). Globally, over two-thirds of river reaches (by length) lack protected areas in
49 their upstream catchments and nine-tenths of river reaches (by length) do not achieve full integrated
50 protection (Abell et al., 2017).
51
52 Terrestrial and freshwater protected areas can also serve as climate change refugia, locations where suitable
53 conditions may persist for the species into the future (e.g. Section 2.6.5.6). In Canada, Mexico, and the U.S.,
54 only a fraction of protected area is located in potential climate change refugia under a 4ºC temperature
55 increase, estimated at 4% (Michalak et al., 2018) to 7% (Batllori et al., 2017). Potential refugia from biome
56 shifts due to climate change under temperature increases of 1.8-3.4ºC cover <1% of the U.S. national park
57 area (Gonzalez et al., 2010b), a fraction that reduces to near zero when climate change is combined with
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1 habitat fragmentation due to land use change (Eigenbrod et al., 2015). Protected areas in boreal ecosystems
2 could serve as refugia for species shifting north in Canada (Berteaux et al., 2018) and Finland (Lehikoinen et
3 al., 2019). Invasive species, habitat loss, and other disturbances in protected areas could be lower than in
4 unprotected areas across Europe (Gallardo et al., 2017) and specifically in Spain (Regos et al., 2016) and in
5 Sri Lanka (Kariyawasam et al., 2020). Protected areas conserve refugia from climate change under a
6 temperature increase of 4ºC, important for biodiversity conservation but limited to <10% of current protected
7 area (medium confidence).
8
9 2.5.3.2 Risks to Ecosystems and Services from Wildfire
10
11 2.5.3.2.1 Future projections of wildfire globally
12 Continued climate change under high emissions scenarios that increase global temperature ~4ºC by 2100
13 could increase global burned area 50% (Knorr et al., 2016b) to 70% (Kloster and Lasslop, 2017) and global
14 mean fire frequency ~30% (Gonzalez et al., 2010b), with increases on one-third (Gonzalez et al., 2010b) to
15 two-thirds (Moritz et al., 2012) of global land and decreases on one-fifth (Gonzalez et al., 2010b; Moritz et
16 al., 2012). Lower emissions that would limit the global temperature increase to <2ºC would reduce projected
17 increases of global burned area to 30% (Lange et al., 2020) to 35% (Kloster and Lasslop, 2017) and
18 projected increases of fire frequency to ~20% (Gonzalez et al., 2010b; Huang et al., 2015). Continued
19 climate change could further lengthen fire weather seasons (IPCC AR6 WGI Chapter 12). Models that
20 combine projected climate change with potential agricultural expansion project decreases in total burned area
21 (Huang et al., 2015; Knorr et al., 2016b; Park et al., 2021). The area of projected increases in burned area
22 and fire frequency due solely to continued climate change is higher for the world as a whole than the area of
23 projected decreases (medium evidence, medium agreement).
24
25 Increased wildfire due to continued climate change increases risks of tree mortality (Sections 2.5.2.6, 2.5.2.7,
26 2.5.3.2), biome shifts (Section 2.5.2.2), and carbon emissions (Sections 2.5.2.10, 2.5.3.4). Wildfire and
27 biome shifts under projected climate change of 4º C above the pre-industrial period, combined with
28 international trade and transport, cause high risks of invasive species across one-sixth of global area,
29 including extensive high-biodiversity regions (Early et al., 2016).
30
31 Wildfire risks to people include death and destruction of homes, respiratory illnesses from smoke (Ford et
32 al., 2018; Machado-Silva et al., 2020), post-fire flooding from areas exposed by vegetation loss, and
33 degraded water quality through increases in sediment flows (Dahm et al., 2015) and chemical precursors of
34 carcinogenic trihalomethanes when water is later chlorinated for drinking (Section 2.5.3.7; Uzun et al., 2020)
35 . Under RCP8.5 and shared socio-economic pathway SSP3 (high population growth, slow urbanisation), the
36 number of people living in fire-prone areas could increase by three-quarters, to 720 million people in 2100,
37 in a projected global population of 12.4 billion people (Knorr et al., 2016b). Lower emissions under RCP4.5
38 could reduce the number of people at risk by 70 million people. In these projections, human population
39 growth increases human exposure to wildfires more than increase in burned area (Knorr et al., 2016c). A
40 global temperature increase <2ºC could increase global population exposure to wildfire by ~30% (Lange et
41 al., 2020). Increased wildfire under continued climate change increases probabilities of human exposure to
42 fire and risks to public health (medium evidence, high agreement).
43
44 2.5.3.2.2 Future projections of wildfire in high-risk areas
45 Regions identified at high risk of increased burned area, fire frequency, or fire weather by multiple global
46 analyses include: Amazon (Gonzalez et al., 2010b; Huang et al., 2015; Knorr et al., 2016c; Burton et al.,
47 2018; Abatzoglou et al., 2019), Mediterranean Europe (Gonzalez et al., 2010b; Burton et al., 2018;
48 Abatzoglou et al., 2019), Arctic tundra (Moritz et al., 2012; Flannigan et al., 2013), western Australia
49 (Gonzalez et al., 2010b; Burton et al., 2018; Abatzoglou et al., 2019), western United States (Gonzalez et al.,
50 2010b; Moritz et al., 2012; Knorr et al., 2016c). Higher-resolution spatial projections indicate high risks of
51 increased wildfire in the Amazon, Australia, boreal ecosystems, Mediterranean Europe, and the United
52 States under climate change (medium evidence, medium agreement).
53
54 In the Amazon, climate change under RCP8.5, combined with high deforestation, could double the area of
55 high fire probability (Fonseca et al., 2019), double burned area by 2050 (Brando et al., 2020) increase burned
56 area 400‒2800% by 2100 (Le Page et al., 2017), and increase fire intensity 90% (De Faria et al., 2017).
57 Lower greenhouse gas emissions (RCP4.5) and reduced deforestation could reduce fire risk to a one-fifth
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1 increase in the area of high fire probability (Fonseca et al., 2019) and a 100‒500% increase in burned area by
2 2100 (Le Page et al., 2017). Moreover, increased fire, deforestation, and drought, acting through vegetation-
3 atmosphere feedbacks, increase risks of extensive forest dieback and potential biome shifts of up to half of
4 Amazon rainforest to grassland, a tipping point that could release an amount of carbon that would
5 substantially increase global emissions (Oyama and Nobre, 2003; Sampaio et al., 2007; Lenton et al., 2008;
6 Nepstad et al., 2008; Malhi et al., 2009; Settele et al., 2014; Lyra et al., 2016; Zemp et al., 2017a; Zemp et
7 al., 2017b; Brando et al., 2020). Continued climate change, combined with deforestation, increases risks of
8 wildfire and extensive forest dieback in the Amazon rainforest (robust evidence, high agreement).
9
10 In Australia, climate change under RCP8.5 increases risks of pyroconvective fire by 20 to 40 days in
11 rangelands of Western Australia, South Australia, and the Northern Territory (Dowdy et al., 2019).
12 Pyroconvective fire conditions could reach more frequently into the more populated areas of New South
13 Wales, particularly at the start of austral summer (Di Virgilio et al., 2019). General circulation models do not
14 agree, however, on projected areas of fire increase in New South Wales (Clarke and Evans, 2019). Increases
15 in heat and potential increases in wildfire threaten the existence of temperature montane rainforest in
16 Tasmania, Australia (Mariani et al., 2019).
17
18 In Mediterranean Europe, climate change of 3ºC could double or triple burned area, while keeping the
19 temperature increase to 1.5ºC could limit burned area increase to 40‒50% (Turco et al., 2018). Under
20 RCP8.5, the frequency of heat-induced fire weather could increase 30% (Ruffault et al., 2020). Severe fire
21 followed by drought could cause biome shifts of forest to non-forest (Batllori et al., 2019) and tree mortality
22 >50% (Dupire et al., 2019).
23
24 In Arctic tundra, boreal forests, northern peatlands, including permafrost areas, climate change under
25 scenarios of 4ºC temperature increase could triple burned area in Canada (Boulanger et al., 2014), double the
26 number of fires in Finland (Lehtonen et al., 2016), increase lightning-driven burned area 30 to 250%
27 (Veraverbeke et al., 2017; Chen et al., 2021a), push half of the area of tundra and boreal forest in Alaska
28 above the burning threshold temperature, and double burned area in Alaska (Young et al., 2017a). Thawing
29 of Arctic permafrost from a projected temperature of 4ºC and resulting wildfire could release 11-200 Gt
30 carbon that could substantially exacerbate climate change (Section 2.5.2.9).
31
32 In the United States, climate change under RCP8.5 could increase burned area 60-80% by 2049 (Buotte et
33 al., 2019) and the number of fires with an area >50 km2 by 300‒400% by 2070 (Barbero et al., 2015). In
34 montane forests in the U.S., climate change under RCP8.5 increases the risk of fire-facilitated conversion of
35 ~7% of forest to non-forest by 2050 (Parks et al., 2019). In California, climate change under a scenario of
36 4ºC temperature increase could double fire frequency in some areas (Mann et al., 2016), but emissions
37 reductions that limit the temperature increase to ~2ºC could keep fire frequency from increasing (Westerling
38 et al., 2011). Carbon dioxide fertilisation and increased temperature under climate change could increase
39 invasive grasses and wildfire in desert ecosystems of the Southwestern United States, where wildfire has
40 historically been absent or infrequent, and increase mortality of the sparse tree cover (Horn and St. Clair,
41 2017; Klinger and Brooks, 2017; Syphard et al., 2017; Moloney et al., 2019; Sweet et al., 2019).
42
43 In summary, under a high emissions scenario that increases global temperature 4ºC by 2100, climate change
44 could increase global burned area 50-70% and global mean fire frequency ~30% with increases on one-third
45 to two-thirds of global land and decreases on one-fifth of global land (medium confidence). Lower emissions
46 that would limit the global temperature increase to <2ºC would reduce projected increases of burned area to
47 ~35% and projected increases of fire frequency to ~20% (medium confidence). Increased wildfire, combined
48 with erosion due to deforestation, could degrade water supplies (high confidence). For ecosystems with
49 historically low fire frequencies, a projected 4ºC global temperature increases risks of fire, contributing to
50 potential tree mortality and conversion of over half of Amazon rainforest to grassland and thawing of Arctic
51 permafrost that could release 11-200 Gt carbon that could substantially exacerbate climate change (medium
52 confidence).
53
54 2.5.3.3 Risks to Ecosystems and Services from Tree Mortality
55
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1 Under continued climate change, increased temperature, aridity, drought, wildfire (Section 2.5.3.2), and
2 insect infestations (Section 2.4.4.3.3) will tend to increase tree mortality across wide parts of the world
3 (McDowell et al., 2020). Boreal and temperate forest loss to fire, wind, and bark beetles could cause more
4 negative than positive effects for most ecosystem services, including carbon storage to regulate climate
5 change (Sections 2.4.4.3, 2.5.2.6, 2.5.2.7, 2.5.3.4), water supply for people (Section 2.5.3.6.1), timber
6 production (Chapter 5), and hazard protection (Thom and Seidl, 2016). In addition, deforestation in tropical
7 and temperate forests can increase local temperatures 0.3° to 2ºC (Hesslerová et al., 2018; Lejeune et al.,
8 2018; Zeppetello et al., 2020) and this effect can extend up to 50 km (Cohn et al., 2019).
9
10 In Amazon rainforests, the relatively lower buffering capacity for plant moisture during drought increases
11 the risk of tree mortality and, combined with increased heat from climate change and fire from deforestation,
12 the possibility of a tipping point of extensive forest dieback and a biome shift to grassland (Oyama and
13 Nobre, 2003; Sampaio et al., 2007; Lenton et al., 2008; Nepstad et al., 2008; Malhi et al., 2009; Salazar and
14 Nobre, 2010; Settele et al., 2014; Lyra et al., 2016; Zemp et al., 2017b; Brando et al., 2020). This could
15 occur at a 4-5ºC temperature increase above the pre-industrial period (Salazar and Nobre, 2010). Under
16 RCP8.5, half of Amazon tropical evergreen forest could shift to grassland through drought-induced tree
17 mortality and wildfire, but lower emissions (RCP4.5) could limit the loss to ~5% (Lyra et al., 2016).
18 Precipitation declines from reduced evapotranspiration inputs after forest loss could cause additional
19 Amazon forest loss of one-quarter to one-third (Zemp et al., 2017a). Similarly, in Guinean tropical deciduous
20 forest in Africa, climate change under RCP8.5 could increase mortality 700% by 2100 or 400% under lower
21 emissions (RCP4.5; Claeys et al., 2019). These projections indicate risks of climate change-induced tree
22 mortality reducing tropical forest areas in Africa and South America up to half under a 4ºC increase above
23 the pre-industrial period, but a lower projection of a 2ºC increase could limit the projected increases in tree
24 mortality (robust evidence, high agreement).
25
26 Temperate and boreal forests possess greater diversity of physiological traits related to plant hydraulics, so
27 they are more buffered against drought than tropical forests (Anderegg et al., 2018). Nevertheless, in
28 temperate forests, drought-induced tree mortality under RCP8.5 could cause the loss of half of northern
29 hemisphere conifer forest area by 2100 (McDowell et al., 2016). In the western United States, one-tenth of
30 forest area is highly vulnerable to drought-induced mortality under RCP8.5 by 2050 (Buotte et al., 2019). In
31 California, increased evapotranspiration in Sierra Nevada conifer forests increases the potential fraction of
32 the area at risk of tree mortality 15‒20% per degree Celsius (Goulden and Bales, 2019). In Alaska, fire-
33 induced tree mortality from climate change under RCP8.5 could reduce the extent of spruce forest (Picea
34 sp.) 8‒44% by 2100 (Pastick et al., 2017). Under RCP8.5, tree mortality from drought, wildfire, and bark
35 beetles could reduce timber productivity of boreal forests in Canada by 2100 below current levels (Boucher
36 et al., 2018; Chaste et al., 2019; Brecka et al., 2020). In Tasmania, projected increases in wildfire (Fox-
37 Hughes et al., 2014) increase risks of mortality in mesic vegetation (Harris et al., 2018b) and threaten the
38 disappearance of the long-lived endemic pencil pine (Athrotaxis cupressoides) (Holz et al., 2015; Worth et
39 al., 2016) and temperate montane rainforest (Mariani et al., 2019). These projections indicate risks of climate
40 change-induced tree mortality reducing some temperate forest areas by half under emissions scenarios of
41 2.5-4ºC above the pre-industrial period (medium evidence, high agreement).
42
43 2.5.3.4 Risk to Terrestrial Ecosystem Carbon Stocks
44
45 Globally, increasing atmospheric CO2 enhances the terrestrial sink but temperature increases constrain it,
46 reflecting biological process understanding, highlighted in previous IPCC reports (high confidence).
47 Analyses of atmospheric inversion model output and spatial climate data indicate a sensitivity of net
48 ecosystem productivity to CO2 fertilisation of 3.1 ± 0.1 to 8.1 ± 0.3 Gt per 100 ppm CO2 (~1°C increase) and
49 a sensitivity to temperature of -0.5 ± 0.2, to -1.1 ± 0.1 Gt per degree Celsius (Fernandez-Martinez et al.,
50 2019). The future of the global land carbon sink (Section 2.4.4.4) nevertheless remains highly uncertain
51 because (i) of regionally complex interactions of climate change and changes in atmospheric CO2 with
52 vegetation, soil and aquatic processes, (ii) episodic events such as heat-waves or droughts (and related
53 impacts through mortality, wildfire or insects, pests and diseases, (Section 2.5.5.2, 2.5.5.3) so far are only
54 incompletely captured in carbon cycle models, and (iii) legacy effects from historic land-use change and
55 environmental changes are incompletely captured but likely to decline in future, and (iv) lateral carbon
56 transport processes such as export of inland waters or erosion are incompletely understood and modelled (
57 AR6 WGI Chapter 5; Pugh et al., 2019a; Friedlingstein et al., 2020; Krause et al., 2020). Enhanced carbon
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1 losses from terrestrial systems further limit the available carbon budget for global warming staying below
2 1.5oC (Rogelj et al., 2018). Analyses of satellite remote sensing and ground-based observations has indicated
3 that between 1982 and 2015 the CO2 fertilisation effect has already declined, implying a negative climate
4 system feedback (Wang et al., 2020c). Peatlands, permafrost regions and tropical ecosystems are particularly
5 vulnerable due to their large carbon stocks in combination with over-proportional warming, increases in
6 heatwaves and droughts and/or a complex interplay of climate change and increasing atmospheric CO2
7 (Section 2.5.2.8, 2.5.2.9, 2.5.3.2).
8
9 Model projections suggest under all warming scenarios a reduction of permafrost extent and potentially large
10 carbon losses (AR6 WGI Chapter 5). Already a mean temperature increase of 2ºC could reduce the total
11 permafrost area extent by ca 5-20% by 2100 (Comyn-Platt et al., 2018; Yokohata et al., 2020). Associated
12 CO2 losses of order of 15 up to nearly 70 GtC by 2100 have been projected across a number of modelling
13 studies (Schneider von Deimling et al., 2015; Comyn-Platt et al., 2018; Yokohata et al., 2020). Limiting the
14 global temperature increase to 1.5oC, compared to 2ºC could reduce projected permafrost CO2 losses by
15 2100 by (median) 24.2 GtC (calculated for 3m depth (Comyn-Platt et al., 2018). Losses are possibly
16 underestimated in those studies that consider only upper permafrost layers. Likewise, the actual committed
17 carbon loss may well be larger (e.g., eventually a loss of ca. 40% of today’s permafrost area extent if climate
18 is stabilised at 2oC above pre-industrial levels) due to the long time-scale of warming in deep permafrost
19 layers (Chadburn et al., 2017). It is unknown at which level of global warming abrupt permafrost collapse
20 estimated to enhance CO2 emission by 40% in 2300 in a high emissions scenario, compared to gradual thaw
21 emissions (Turetsky et al., 2020) would have to be considered an important additional risk. Large
22 uncertainties arise also from interactions with changes in surface hydrology and/or northward migrating
23 woody vegetation as climate warms, which could dampen or even reverse projected net carbon losses in
24 some regions (McGuire et al., 2018a; Mekonnen et al., 2018; Pugh et al., 2018) so overall there is low
25 confidence on how carbon-permafrost interactions will affect future carbon cycle and climate, although net
26 carbon losses and thus positive (amplifying feedbacks) are likely (Sections 2.5.2.10, 2.5.3.5; Shukla et al.,
27 2019). See also AR6 WGI (Chapter 5) for discussion of impacts of higher emission and warming scenarios.
28
29 Peatland carbon is estimated as ca. 550‒1000 GtC in northern latitudes (many of these peatlands would be
30 found in permafrost regions) (Turetsky et al., 2015; Nichols and Peteet, 2019) and > 100 GtC in tropical
31 regions (Turetsky et al., 2015; Dargie et al., 2017). Both for northern mid- and high-latitude and for tropical
32 peatlands a shift from contemporary CO2 sinks to sources were simulated in high warming scenarios (Wang
33 et al., 2018a; Qiu et al., 2020). Due to the lack of large-scale modelling studies, the confidence on climate
34 change impacts on peat carbon uptake and emissions is low. The largest risk to tropical peatlands is expected
35 to arise from drainage and conversion to forestry or agriculture, outpacing impacts of climate change (Page
36 and Baird, 2016; Leifeld et al., 2019; Cooper et al., 2020) although the magnitude of possible carbon losses
37 are uncertain and depend strongly on socio-economic scenarios. (Sections 2.4.3.8, 2.4.4.2; 2.4.4.4.2, 2.5.2.8)
38
39 For tropical and sub-tropical regions the interplay of atmospheric CO2 with precipitation and temperature
40 becomes of particular importance for future carbon uptake, since in warm and dry environments, elevated
41 CO2 fosters plants with C3 photosynthesis and enhances their water use efficiency relative to C4 species
42 (Moncrieff et al., 2014a; Midgley and Bond, 2015; Knorr et al., 2016a). As a consequence, enhanced woody
43 cover is expected to occur in future especially in mesic savannas, while in xeric savannas an increase in
44 woody cover would occur in regions with enhanced precipitation (Criado et al., 2020). Even though semi-
45 arid regions have dominated the recent decades’ global trend in land CO2 uptake (Ahlström et al., 2015), so
46 far most studies that investigated future climate change impacts on savanna ecosystems have concentrated on
47 changes in areal extent (2.5.2.5), rather than on carbon cycling, with medium confidence on increasing
48 woody:grass ratios (Moncrieff et al., 2014a; Midgley and Bond, 2015; Moncrieff et al., 2016; Criado et al.,
49 2020). Increases in woody vegetation in what is now grass-dominated would possibly come with a carbon
50 benefit, for instance a broad range of future climate and CO2 changes were found to enhance vegetation C
51 storage in Australian savannas (Scheiter et al., 2015). Results from a number of field experiments indicate
52 however, that impacts on total ecosystem carbon storage may be smaller, due to a loss in belowground
53 carbon (Coetsee et al., 2013; Wigley et al., 2020). Nunez et al., 2021) critique existing incentives to promote
54 invasion of non-native trees into treeless areas as a means of carbon sequestration, raising doubts about the
55 effects on fire, albedo, biodiversity and water yield (see Box 2.2).
56
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1 Substantial climate-change driven impacts on tropical tree cover and vegetation type are projected in all
2 studies, irrespective of whether or not the degree amounts to a forest “dieback” (Sections 2.4.3.6, 2.4.4.3;
3 2.5.2.6, 2.5.3.3; AR6 WGI Chapter 5) (Davies-Barnard et al., 2015; Wu et al., 2016a; Zemp et al., 2017a).
4 Accordingly, models also suggest a continuation of tropical forests acting as carbon sinks (Huntingford et
5 al., 2013; Mercado et al., 2018). A recent study, combining field plot data with statistical models, (Hubau et
6 al., 2020) indicates that in the Amazonian and possibly also in the African forest the carbon sink in
7 aboveground biomass has already declined over the three decades to 2015. This trend is distinct in the
8 Amazon, whereas data from Africa suggest a possible decline after 2010. the authors estimate the vegetation
9 carbon sink in 2030-2040 to decline to zero (-0.5–0-46) Pg C a-1 in the Amazon and 0.26 (0.04–0.47, a loss
10 of 14% compared to present) Pg C a-1 in Africa. Their results suggest that CO2 fertilisation is over time
11 outweighed by impacts of higher temperatures and drought, enhancing tree mortality and diminishing
12 growth. The degree of thermal resilience of tropical forest still remains uncertain, however (Sullivan et al.,
13 2020).
14
15 The lack of simulation studies that seek to quantify all important interacting factors (CO2, drought and fire)
16 for future carbon-cycling in savannas and tropical forests, and the apparent disagreement between trends
17 projected in models compared to data-driven estimates results in low confidence regarding the direction or
18 magnitude of carbon flux and pool size changes. Similar to tropical peatlands, given projected human
19 population growth and socio-economic changes. the continued conversion of forests and savannas into
20 agricultural or pasture systems very likely poses a significant risk of rapid carbon loss which will amplify
21 climate change induced risks substantially (high confidence) (Sections 2.5.2.10, 2.5.3.5; Aragao et al., 2014;
22 Searchinger et al., 2015; Aleman et al., 2016; Nobre et al., 2016).
23
24 The impacts of climate-induced altered animal composition and trophic cascades on global land ecosystem
25 carbon cycling are as yet unquantified (Schmitz et al., 2018) even though climate change is expected to lead
26 to shifts in consumer-resource interactions that also contribute to losses of top-predators or top herbivores
27 (Sections 2.4.2.2, 2.5.1.3, 2.5.4; Lurgi et al., 2012; Damien and Tougeron, 2019). Cascading trophic effects
28 triggered by top predators or the largest herbivores propagate through food webs and reverberate through to
29 the functioning of whole ecosystems, altering notably productivity, carbon and nutrient turnover and net
30 carbon storage (medium confidence) (Wilmers and Schmitz, 2016; Sobral et al., 2017; Stoner et al., 2018).
31 Across different field experiments, the ecosystem consequences of the presence or absence of herbivores and
32 carnivores have been found to be quantitatively as large as the effects of other environmental change drivers
33 such as warming, enhanced CO2, fire or variable nitrogen deposition (medium confidence) (Hooper et al.,
34 2012; Smith et al., 2015). Some local and regional-scale modelling experiments have begun to explore
35 animal impacts on vegetation dynamics and carbon and nutrient cycling (Pachzelt et al., 2015; Dangal et al.,
36 2017; Berzaghi et al., 2019). Given that turnover rate is a chief factor that determines future land ecosystem
37 carbon dynamics and hence carbon-climate feedbacks (Friend et al., 2014). To improve projections, it is
38 imperative to better quantify the broader role of carnivores, grazers, browsers, and the way these interact in
39 global studies of how ecosystems respond to climate change.
40
41 2.5.3.5 Feedbacks between Ecosystems and Climate
42
43 The possibility of feedbacks and interactions between climate drivers and biological systems or ecological
44 processes was identified as a significant emerging issue in AR5, and has since also been highlighted in the
45 CRCCL and the Special Report on 1.5oC. It is virtually certain that land cover changes affect regional and
46 global climate through changes to albedo, evapotranspiration and roughness (very high confidence) (Perugini
47 et al., 2017). There is growing evidence that biosphere-related climate processes are being affected by
48 climate change in combination with disturbance and land use change (high confidence) (Jia et al., 2019). It is
49 virtually certain that land surface change caused by disturbances such as forest fire, hurricanes, phenological
50 changes, insect outbreaks and deforestation affect carbon, water, and energy exchanges, thereby influencing
51 weather and climate (very high confidence) (Table 2.4; Figure 2.10; Bright et al., 2013; Brovkin et al., 2013;
52 Naudts et al., 2016; Prăvălie, 2018).
53
54
55
56 Table 2.4: Terrestrial and freshwater ecosystem feedbacks which affect the Earth's climate system dynamics, following
57 (Prăvălie, 2018).
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Perturbation Implications for Warming/Feedback Mechanism
the Earth's Climate System Dynamics
Phenological change Increased primary productivity and plant growth with CO2 fertilisation (Mao et al., 2016; Wang et
al., 2018a); Increasing growing season length (Peñuelas et al., 2009; Barichivich et al., 2013);
reduced diurnal temperature range through evapotranspiration (mid-latitudes) and albedo (high
latitudes) caused by vegetation greening (Jeong et al., 2011); increased CO2 storage in biomass
(cooling) (Keenan et al., 2014); Reduced albedo in snow-covered regions as canopies become taller
and darker (warming); increased evapotranspiration, a key component of the global water cycle and
energy balance which influences global rainfall, temperature, and atmospheric motion (Zeng et al.,
2017).
Insect outbreaks Reduced carbon uptake and storage (warming); Increased surface albedo (cooling) (Landry et al.,
2016); increased CO2 emissions (warming); decreased leaf area index and gross primary
productivity (Ghimire et al., 2015), leading to reduced evapotranspiration and increased land
surface temperature (Bright et al., 2013).
Range shifts Reduced albedo in snow-covered regions as trees expand poleward (warming) (Chae et al., 2015);
enhanced permafrost thawing; expansion of insect outbreak range, increasing forest impact
(Pureswaran et al., 2018); biome dependent changes in albedo and evapotranspiration regimes
(Naudts et al., 2016). Reduction in snow and ice albedo in freshwater due to loss of ice (warming)
(Lang et al., 2018).
Die-off and large-scale Decreased Gross primary productivity (GPP); decline in carbon storage (warming); increased CO2
mortality events emissions; increased solar radiation, reduced soil moisture, higher surface runoff; albedo effects
(Lewis et al., 2011; Prăvălie, 2018)
Deforestation Reduced carbon storage (warming) (Pugh et al., 2019a); increase in (regional) surface air
temperature due to reduced evaporation (less cooling); increased albedo in high-latitude systems
(regional radiative cooling) (Loranty et al., 2014); increased air temperature and diurnal
temperature variation (Alkama and Cescatti, 2016), locally and globally (Winckler et al., 2019);
reduced precipitation (Perugini et al., 2017); decreased biogenic volatile organic compounds
(BVOC) and aerosol emissions (warming through direct and indirect aerosol effects; cooling
associated with reduction in atmospheric methane (Jia et al., 2019)
Forest degradation Reduced carbon storage (warming) (de Paula et al., 2015; Bustamante et al., 2016; de Andrade et
al., 2017; Mitchard, 2018)
Fragmentation Carbon losses because biomass is less developed in forest edges (Pütz et al., 2014; Chaplin-Kramer
et al., 2015; Haddad et al., 2015)
Air pollution Decreased plant productivity, transpiration and carbon sequestration in forest with lower biomass
due to ozone toxicity (Sitch et al., 2007; Ainsworth et al., 2012); increased (regional) productivity
due to increase in diffuse solar radiation caused by terrestrial aerosols (Xie et al., 2021)
Declining populations of Changes to physical and chemical properties of organic matter, soils and sediments influence
megafauna carbon uptake and storage (Schmitz et al., 2018); increased or decreased carbon storage biomass
and carbon storage, with differences across biomes determined by floristic structure and animal size
(Bello et al., 2015; Osuri et al., 2016; Peres et al., 2016; He et al., 2017; Berzaghi et al., 2018;
Schmitz et al., 2018; He et al., 2019)
Fire Increased carbon and aerosol emissions(van der Werf et al., 2017); surface warming (Liu et al.,
2019b); albedo effect dependent on ecosystem and species-level traits (Rogers et al., 2015; Chen et
al., 2018a) (initial albedo decrease post-fire; increased albedo where snow exposure is increased by
canopy removal and species composition changes during recovery); black carbon deposition on
snow and sea ice (short-term) (Randerson et al., 2006); indirect increases in carbon emissions due
to soil erosion (Caon et al., 2014)
Change in forest Reduced carbon storage due to decline in biomass (warming) (McIntyre et al., 2015)
composition
Woody encroachment in Reduced production, increased water use, reduced albedo and altered land-atmosphere feedbacks;
non-forested ecosystems increased carbon storage in woody savannas (Zhou et al., 2017; Mureva et al., 2018); Uncertain
feedbacks to C cycle (some suggest an increase, others a decrease)
Net Primary Productivity Reduced albedo following high-latitude expansion of trees caused by photosynthetic enhancement
(NPP) shifts of growth (cooling); increased photosynthesis and net ecosystem production (NEP) (Fernandez-
Martinez et al., 2019); increased NPP in N‐limited ecosystems due to increased nitrogen deposition
from agriculture and combustion (Du and de Vries, 2018; Schulte-Uebbing and de Vries, 2018);
Nutrient limited lakes are likely to become less productive, while nutrient rich lakes are likely to
become more productive due to warming induced prolongation of stable stratification (Adrian et
al., 2016; Kraemer et al., 2017).
Biogeochemical shifts Decline in carbon storage due to nitrogen limitation in N limited systems (warming)(Reich et al.,
2014; Wieder et al., 2015); increased carbon storage on land(Peñuelas et al., 2013) and in lakes
(Heathcote et al., 2015; Mendonça et al., 2017); Increase in CO2 and CH4 emissions from
freshwater ecosystems due to increased eutrophication (DelSontro et al., 2018), the imbalance
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between losses and gains of CO2 by photosynthesis and respiration (metabolic theory of ecology),
enhanced emissions from exposed river and lake sediments during droughts and re-wetting (Marcé
et al., 2019; Keller et al., 2020), enhanced CH4 ebullition of seasonally hypoxic lakes (Aben et al.,
2017; DelSontro et al., 2018; Bartosiewicz et al., 2019; Beaulieu et al., 2019; Sanches et al., 2019),
increased transfer of organic carbon from land to water (particularly in permafrost areas) (Wauthy
et al., 2018)
1
2
3
4 Figure 2.10: Terrestrial ecosystem feedbacks, which affect the Earth's climate system dynamics. Perturbations and
5 implications for climate system dynamics (warming/cooling) are shown for the three global forest biomes (adapted
6 from Figure 5, Prăvălie, 2018). The strength of the mechanism is estimated in general terms based on the magnitude of
7 carbon storage and evaporative cooling processes that characterise each forest biome (Bonan, 2008). Carbon storage
8 includes forest biomass, without accounting for carbon dynamics in soil, peat and underlying permafrost deposits.
9 Implications of biogeochemical shifts were only estimated in relation to the intensification of the carbon cycle and
10 increase in biomass at high latitudes, assuming N availability for the stoichiometric demands of forest vegetation).
11
12
13 Feedbacks can be positive or negative (i.e., amplify or dampen the original forcing), vary spatially and
14 seasonally, and act over large geographic areas and long time periods (>decades), making them difficult to
15 observe and quantify directly (AR6 WGI Chapter 5; Schimel et al., 2015). Due to the positive impacts of
16 CO2 on vegetation growth and ecosystem carbon storage (high confidence) (Sections 2.4.4.4; 2.5.5.4; AR6
17 WGI chapter 5), the associated climate feedback is negative (increased removal of atmospheric CO2 and
18 dampened warming, compared to absence of the feedback). By contrast, projected global losses of carbon in
19 warmer climates (AR6 WGI Chapter 5) imply a positive climate feedback. Chapter 5 of WGI assesses an
20 overall increase in land carbon uptake through the 21st century. However, the overall strength of the carbon
21 cycle-climate feedback remains very uncertain. One of the underlying reasons may be complex interactions
22 with ecosystem water balance and nitrogen and phosphorous availability, which are poorly constrained by
23 observational evidence and incompletely captured in Earth System Models (AR6 WGI Chapter 5, Section
24 2.5.2.10; Huntzinger et al., 2017).
25
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1 Land ecosystems contribute substantially to global emissions of nitrous-oxides and methane. As with CO2,
2 these emissions respond both directly and indirectly to atmospheric CO2 concentration and climate change,
3 which gives rise to potential additional biogeochemical feedbacks in the climate system. A large part of these
4 emissions stem from land and water management, such as fertiliser application, rice production, aquaculture
5 or animal husbandry (Jia et al., 2019). However, nearly 60% of total nitrous-oxide emissions (2007-2016) is
6 estimated to stem from natural ecosystems, especially in the tropics (AR6 WGI Chapter 5; Tian et al., 2019),
7 while freshwater wetlands and peatlands are estimated to contribute between 83% (top-down estimates) and
8 40% (bottom up estimates) of total natural CH4.(and 31% or 20% of total methane emissions, respectively)
9 for the period 2008-2017 (AR6 WGI Chapter 5). Median CH4 emissions from northern latitude wetlands in
10 2100 were estimated to be 12.1 and 13.5 Pg C in emission scenarios leading to 1.5oC and 2oC warming,
11 respectively (Comyn-Platt et al., 2018). Likewise, global warming was attributed to soil N2O emission
12 increases since the pre-industrial period of 0.8 (0.3‒1.3) Tg N a-1 (Tian et al., 2020). Overall, climate
13 feedbacks from future altered land ecosystem emissions of CH4 or N2O are uncertain, but expected to be
14 small (AR6 WGI Chapter 5).
15
16 Changes in regional biodiversity are integral parts of ecosystem-climate feedback loops, including and
17 beyond carbon-cycle processes (Figure 2.10; Table 2.4). For instance, the impacts of climate-induced altered
18 animal composition and trophic cascades on ecosystem carbon turnover (see Sections 2.4.4.4; 2.5.3.4) could
19 be a substantive contribution to carbon-climate feedbacks (low confidence). Additional surface-atmosphere
20 feedbacks that arise from changes in vegetation cover and subsequently altered albedo, evapotranspiration or
21 roughness (often summarised as biophysical feedbacks) can be regionally relevant and could amplify or
22 dampen vegetation cover changes (Jia et al., 2019).
23
24 Climate-induced shifts towards forests in what is currently tundra would be expected to reduce regional
25 albedo especially in spring but also during parts of winter when trees are snow-free (whereas tundra
26 vegetation would be covered in snow), which amplifies warming regionally (high confidence) (Perugini et
27 al., 2017; Jia et al., 2019). Trees would also enhance momentum absorption compared to low tundra
28 vegetation thus impacting surface-atmosphere mixing of latent and sensible heat fluxes (Jia et al., 2019).
29 Boreal forests insulate and stabilize permafrost and reduce fluctuations of ground temperature: the amplitude
30 of variation of ground surface temperatures was 28°C in a forested site, compared to 60°C in nearby
31 grassland (Section 2.5.2.7; Bonan, 1989; Stuenzi et al., 2021a; Stuenzi et al., 2021b). Likewise, a shift in
32 moist tropical forests towards vegetation with drought-tolerant traits could possibly reduce
33 evapotranspiration, increase albedo, alter heat transfer at the surface and lead to a negative feedback to
34 precipitation (Section 2.5.2.6; Jia et al., 2019). In savannas, restoration of woody vegetation has been shown
35 to enhance cloud formation and precipitation in response to enhanced transpiration and turbulent mixing,
36 leading to a positive feedback on woody cover (Syktus and McAlpine, 2016). While this has not yet been
37 systematically explored, similar feedbacks might also emerge from a CO2-induced woody cover increase in
38 savannas (low confidence) (Section 2.5.2.5).
39
40 Since biophysical feedbacks can contribute to both surface temperature warming or cooling, analyses so far
41 suggest that, at global scale, the net impact on climate change is small (Perugini et al., 2017; Jia et al., 2019),
42 unless these feedbacks also accelerate vegetation mortality and lead to substantive carbon losses (Zemp et
43 al., 2017a; Lemordant and Gentine, 2019). More than one third of the Earth’s land surface has at least 50%
44 of its evapotranspiration regulated by vegetation, and in some regions between 40 and >80% of the land’s
45 evaporated water is returned to land as precipitation. Locally, both, direct human-mediated as well as climate
46 change-mediated vegetation cover change can therefore notably affect annual average freshwater availability
47 to human societies, especially if negative feedbacks amplify vegetation cover reduction, reduced
48 evapotranspiration and reduced precipitation (medium confidence) (Keys et al., 2016; Keys and Wang-
49 Erlandsson, 2018).
50
51 Since AR5, freshwater ecosystems (lakes, reservoirs, rivers, ponds) have been increasingly recognised as
52 important sources of greenhouse gas emissions (CO2, CH4, N2O) into the atmosphere. Key mechanisms
53 which contribute to rising GHG emissions from freshwater ecosystems are the temperature imbalance
54 between photosynthesis and respiration (respiration increases more than photosynthesis with rising
55 temperature), CO2 and CH4 emissions from exposed sediments during droughts, increased matter transport
56 from land to water, changes in water retention time in rivers and lakes, and temperature effects on lake
57 stratification and anoxia, favouring CH4 emissions.
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1
2 DelSontro et al. (2018) assembled the largest global dataset to date on emission rates from lakes of CO2, CH4
3 and N2O and found that they co-vary with lake size and trophic state. They estimated that moderate global
4 increases in eutrophication of lakes could translate to 5–40% increases in the GHG effects in the atmosphere.
5 Moreover, they estimated that greenhouse gas emissions from lakes and impoundments in past decades
6 accounted for 1.25-2.30 Pg C-CO2 yr-1 (DelSontro et al., 2018), thus around 20% of global burning fossil-
7 fuel CO2 emission (9.4 PgC-CO2 yr-1 (Friedlingstein et al., 2020).
8
9 Global warming will strongly enhance freshwater CH4 emissions through a disproportionate increase in
10 ebullition (gas flux) by 6–20% per 1°C increase in water temperature (Aben et al., 2017). It can be expected
11 that ongoing eutrophication enhanced by climate change-related increases in sediment nutrient release and
12 organic carbon and nutrient loading from catchments will enhance CH4 ebullition at a global scale (Aben et
13 al., 2017; DelSontro et al., 2018; Bartosiewicz et al., 2019; Beaulieu et al., 2019; Sanches et al., 2019). The
14 strongest increase in ebullition is expected in shallow waters where sediment temperatures are strongly
15 related to atmospheric temperature (Aben et al., 2017). Given that small ponds and shallow lakes are the
16 most abundant freshwater ecosystems globally they may become hot spots of CH4 ebullition in the future
17 (Aben et al., 2017). On average CH4, CO2, N2O account for 75%, 23, and 2% of the total CO2 equivalent
18 emissions, respectively in lakes (DelSontro et al., 2018).
19
20 Further, the exposure of lake and river sediments during droughts activates decomposition of buried organic
21 carbon. In dry river beds, mineralisation of buried organic matter is likely to increase with climate change as
22 anoxic sediments are oxygenated downwards during drying along with pulses of microbial activity following
23 rewetting of desiccated sediment. Conservative estimates indicate that adding emissions from exposed
24 sediments of dry inland waters across diverse ecosystem types and climate zones to current global estimates
25 of CO2 emissions could result in a 6% (~0.12 Pg C y−1) increase of total inland water CO2 emission rates
26 covering streams and rivers (334 mmol m-2 day-1), lakes and reservoirs (320 mmol m-2 day-1) and small
27 ponds (148 mmol m-2 day-1) (Marcé et al., 2019; Keller et al., 2020).
28
29 Overall, uncertainty in the quantity of carbon fluxes within freshwater ecosystems and between terrestrial
30 and freshwater systems and subsequent emissions to the atmosphere remain very high (Raymond et al.,
31 2013; Catalán et al., 2016; Stanley et al., 2016; Evans et al., 2017; Drake et al., 2018; Seekell et al., 2018;
32 Sanches et al., 2019; Bodmer et al., 2020; Keller et al., 2020) (see Table 2.SM., see also Chapter 5 of WGI).
33 Projections of carbon fluxes are e.g. challenged by the complex interaction between rising water
34 temperature, loss of ice, changes in hydrology, ecosystem productivity, increase in extreme events, and
35 variation in terrestrial matter transport. While we are still short in empirical data, particularly in the tropics
36 (DelSontro et al., 2018), improvements in sensor technology (Eugster et al., 2011; Gonzalez-Valencia et al.,
37 2014; Maeck et al., 2014; Delwiche et al., 2015) and the use of statistically robust survey designs (Beaulieu
38 et al., 2016; Wik et al., 2016) have improved the accuracy of GHG emission rate measurements in freshwater
39 ecosystems. Global networks such as GLEON (Global Lakes Ecological Observatory Network) increasingly
40 allow a global view of carbon fluxes improving estimates of the contribution of freshwater ecosystems to
41 global GHG emissions to the atmosphere.
42
43 In summary (Drake et al., 2018) aggregated contemporary estimates of CO2 and CH4 emissions from
44 freshwater ecosystems with global estimates made by (Raymond et al., 2013) and arrived at an estimate of
45 3.9 Pg C yr-1. Rivers and streams accounted for 85% of the emissions and lakes and reservoirs for 15%
46 (Raymond et al., 2013). This trend will continue under scenarios of nutrient loading to inland waters over the
47 next century where inland water increased CH4 emission has an atmospheric impact of 1.7–2.6 Pg C-CO2-eq
48 y−1, which is equivalent to 18–33% of annual CO2 emissions from burning fossil fuels (medium evidence,
49 medium agreement, medium confidence) (Beaulieu et al., 2019). For comparison, annual uptake of CO2 in
50 land ecosystems is estimated as 3.4 (± 0.9) PgC/yr (Friedlingstein et al., 2020). The freshwater numbers
51 combine CO2 and CH4 and are thus not directly comparable. However, they are indicative for the importance
52 to account better for freshwater systems in global carbon budgets.
53
54 2.5.3.6 Risks to Freshwater Ecosystem Services: Drinking Water, Fisheries and Hydropower
55
56 AR5 named water supply and biodiversity as freshwater ecosystem services vulnerable to climate change.
57 We discuss risks to these and to additional services identified by model projections based both on climate-
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1 change scenarios (Schröter et al., 2005; Boithias et al., 2014; Huang et al., 2019; Jorda-Capdevila et al.,
2 2019) and on the Common International Classification of Ecosystem Services (high agreement, high
3 confidence) (CICES, 2018). Effects of floods, droughts, permafrost and glacier melting on global changes in
4 water quality, particularly with respect to contamination with pollutants, are described in Section 4.2.6.
5
6 2.5.3.6.1 Risks to quantity and quality of drinking water
7 Forests and other vegetated ecosystems assist production of drinkable water by facilitating infiltration of
8 rainfall and snowfall into the ground, where water either moves through the soil saturated zone to supply
9 streams and other surface waters or infiltrates further to recharge groundwater aquifers (Ellison et al., 2012;
10 Bonnesoeur et al., 2019). Globally, 4 billion people depend on forested watersheds for drinking water
11 (Mekonnen and Hoekstra, 2016). Chapter 4 assesses the physical science of water supply, including
12 precipitation, runoff, and hydrology, and social aspects of human water use. This section assesses ecological
13 aspects of risks to freshwater supplies for people.
14
15 Reduction of vegetation cover following wildfires (Section 2.5.5.2) and tree mortality (Section 2.5.5.3) can
16 reduce long-term water infiltration, increase soil erosion and flash floods, and release sediment that degrades
17 drinking water quality. Widlfires increase impacts of extreme precipitation events due to climate change,
18 which contribute to increased surface runoff and hence to increased risks of land erosion, landslides and
19 flooding (Ebel et al., 2012; Robinne et al., 2020). Under current conditions, nearly half of global land area is
20 at moderate to high risk of water scarcity due to wildfires (Robinne et al., 2018; Robinne et al., 2020). From
21 1984 to 2014 wildfires in the western USA affected 6-11% of stream and river length (Ball et al., 2021).
22 Under a high emissions scenario of a 3.5ºC temperature increase post-fire erosion across the western USA
23 could double sedimentation and degrade drinking water quality in one-third of watersheds by 2050 (Sankey
24 et al., 2017). In Brazil, post-fire vegetation loss tends to increase runoff, reduce infiltration, and reduce
25 groundwater recharge and flow of springs (Rodrigues et al., 2019). Runoff from wildfires can contain
26 dissolved organic carbon precursors for the formation of carcinogenic trihalomethanes during water
27 chlorination for drinking (Uzun et al., 2020), plus chromium, mercury, selenium, and other toxic trace metals
28 (Burton et al., 2016; Burton et al., 2019).
29
30 Net effects of deforestation and afforestation on runoff and water supply depend on local factors, leading to
31 conflicting evidence for effects of land cover change (Ellison et al., 2012; Chen et al., 2021b), but
32 combinations of climate change and deforestation are projected to reduce water flows (Olivares et al., 2019).
33 In southern Thailand, the combination of conversion of forest to rubber plantations and a one-third increase
34 in rainfall could increase erosion and sediment load 15% (Trisurat et al., 2016). In the watershed that
35 supplies São Paulo, Brazil, afforestation could increase water quantity and quality (Ferreira et al., 2019). In
36 most regions with dry or Mediterranean subtropical climates, climate change reduces renewable surface
37 water and groundwater resources significantly (Doell et al., 2015). In northeast Spain, reduced precipitation
38 and vegetation cover under a high emissions scenario of a 3.5ºC temperature increase could reduce drinking
39 water supplies by half by 2100 (Bangash et al., 2013).
40
41 Changes in algal biomass development and spread of cyanobacteria blooms, related to global warming,
42 resemble those triggered by eutrophication with well-known negative effects on the services lakes provide,
43 particularly for drinking water provision and recreation (robust empirical evidence, high agreement, high
44 confidence) (Carvalho et al., 2013; Adrian et al., 2016; Gozlan et al., 2019).
45
46 Based on a 10% increase in precipitation, (de Wit et al., 2016) estimated increased mobilisation of organic
47 carbon from soils to freshwaters by at least 30%, demonstrating the importance of climate wetting for the
48 carbon cycle. Browning negatively affects the taste of drinking water and may be difficult to address
49 (Kothawala et al., 2015; Mineau et al., 2016; Kritzberg et al., 2020). It also often reduces attractives for
50 recreational purposes, especially swimming (Arthington and Hadwen, 2003; Keeler et al., 2015). Based on a
51 worst-case climate scenario until 2030 (Weyhenmeyer et al., 2016) projected an increase in browning of
52 lakes and rivers in boreal Sweden by a factor of 1.3. The chemical character of dissolved organic matter, as
53 modified by climate change (Kellerman et al., 2014), determines its amenability to removal by water
54 treatment (Ritson et al., 2014). Therefore, in order to provide safe and acceptable drinking water, more
55 advanced, more expensive and more energy/resource intensive technical solutions may be required
56 (Matilainen et al., 2010).
57
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FINAL DRAFT Chapter 2 IPCC WGII Sixth Assessment Report
1 In summary, climate change increases risks to the integrity of watersheds and provision of safe, acceptable
2 freshwater to people (medium evidence, medium agreement).
3
4 2.5.3.6.2 Risks to freshwater fisheries and biodiversity
5 Climate change will increase water temperatures and decrease dissolved oxygen levels (Section 2.3.1)
6 impacting freshwater fisheries which form an important ecosystem service (Vári et al., 2021). People living
7 in the vicinity of cold lakes will be affected by projected losses of ice. In a worst-case scenario (air
8 temperatures increase of 8°C), 230,400 lakes and 656 million people in 50 countries will be impacted (Reid
9 et al., 2019; Sharma et al., 2019). Winter ice fishing (Orru et al., 2014), transportation via ice roads (Prowse
10 et al., 2011) and cultural activities (Magnuson and Lathrop, 2014) are ecosystem services at stake with
11 ongoing loss of lake ice.
12
13 Eutrophication of central European lakes has wiped out a significant proportion of the endemic fish fauna
14 (Vonlanthen et al., 2012), so climate-induced further eutrophication is expected to represent an additional
15 threat to fish fauna and commercial fisheries (Ficke et al., 2007). Given that the ecological consequences of
16 lake warming may be especially strong in the tropics ( Section 2.3.1.1), ecosystem services may be most
17 affected there. Tropical lakes support important fisheries (Lynch et al., 2016a economic; McIntyre et al.,
18 2016) providing critical sources of nutrition to adjacent human populations. These lakes are especially prone
19 to loss of deep-water oxygen due to warming, with adverse consequences for fisheries productivity and
20 biodiversity (medium evidence, medium confidence) (Lewis Jr, 2000; Van Bocxlaer et al., 2012).
21
22 Tropical lakes tend to be hotspots of freshwater biodiversity (Vadeboncoeur et al., 2011; Brawand et al.,
23 2014; Sterner et al., 2020); ancient tropical lakes such as Malawi, Tanganyika, Victoria, Titicaca, Towuti
24 and Matano hold thousands of animal species found nowhere else (Vadeboncoeur et al., 2011). While
25 biodiversity and several ecosystem services can be considered synergistic (food webs, tourism, aesthetical
26 and spiritual value (Langhans et al., 2019), others can be considered antagonistic in case of a strong
27 ecosystem service demand (such as water abstraction, water use, food security in terms of over-exploitation).
28 Here the balance between biodiversity and ecosystem services is key (Langhans et al., 2019), where
29 biodiversity can be integrated into water policy through Integrated Water Resource Management (IWRM)
30 towards nature-based solutions (Ligtvoet et al., 2017)
31
32 2.5.3.6.3 Risks to hydro power and erosion control
33 River banks, riparian vegetation and macrophyte beds play important roles in erosion control through
34 reducing current velocities, increasing sedimentation and reducing turbidity (Madsen et al., 2001). Rates of
35 flow in rivers affect and inland navigation (Vári et al., 2021). Changing seasonality in snow-dominated
36 basins is expected to enhance hydropower production in winter, but decrease it during summer (Doell et al.,
37 2015). Glacier melt changes hydrological regimes, sediment transport, and biogeochemical and contaminant
38 fluxes from rivers to oceans, profoundly influencing ecosystem services that glacier-fed rivers provide,
39 particularly provision of water for agriculture, hydropower, and consumption (Milner et al., 2017). Loss of
40 glacial mass and snowpack has already impacted flow rates, quantities and seasonality (Hock et al., 2019);
41 see AR6 WGII, Chapter 4 Water). Meltwater yields from glacier ice are likely to increase in many regions
42 during the next decades, but decrease thereafter as glaciers become smaller and smaller and finally disappear
43 (Hock et al., 2019).
44
45 2.5.4 Key Risks to Terrestrial and Freshwater Ecosystems from Climate Change
46
47 Among numerous risks to terrestrial and freshwater ecosystems from climate change, this IPCC chapter
48 identified five phenomena as the most fundamental risks of climate change to ecosystem integrity and the
49 ecosystem services that support human well-being that are also quantified: species losses to ecosystems,
50 increased wildfire, increased tree mortality, ecosystem carbon losses, and ecosystem structure change (Table
51 2.5, Table 2.S.4, Figure 2.11). These key risks form part of the overall assessment of key risks in Chapter 16.
52 The IPCC Fifth Assessment Report chapter on terrestrial ecosystems (Settele et al., 2014) had also identified
53 three of these key risks – species extinctions, tree mortality, ecosystem carbon losses – and a fourth –
54 invasion by non-native species. This IPCC chater assesses impacts of climate change on invasive species in
55 multiple sections with respect to different processes or systems (e.g. in Section 2.4.2.3.3), and here includes
56 this aspect in a new broader key risk of ecosystem structure change. The IPCC Fifth Assessment Report had
57 included wildfire as a mechanism of the ecosystem carbon loss key risk. Based on additional research and
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1 field experience with major wildfires since then, this IPCC chapter sets wildfire apart as a specific key risk to
2 ecosystem integrity and human well-being. These different measures of risk are interconnected but approach
3 assessment of risk to terrestrial and freshwater ecosytems from different angles, using complementary
4 metrics.
5
6 Species are the fundamental unit of ecosystems. As species become rare, their roles in the functioning of the
7 ecosystem diminishes, and disappears altogether if they go locally extinct (high confidence) (Isbell et al.,
8 2015; Chen et al., 2018b; van der Plas, 2019; Wang et al., 2021b). Loss of species and functional groups
9 reduces the ability of an ecosystem to provide services, and lowers its resilience to climate change (high
10 confidence) (Section 2.6.7; Elmqvist et al., 2003; Cadotte et al., 2011; Harrison et al., 2014; Carlucci et al.,
11 2020). For example, among crop systems, a key factor to succesful pollination is the phylogenetic diversity
12 of bee species available, more than total abundances (Drossart and Gérard, 2020). Because many species
13 have obligate interactions with, or are resources for, other species (e.g. predators and their prey, insects and
14 their host plants, plants and their mycorrhizae symbionts), loss of one species affects risk to another species
15 and, ultimately, ecosystem functioning (Mahoney and Bishop, 2017)
16
17 Global rates of species extinction are accelerating dramatically (Barnosky et al., 2011), with approximately
18 10% of species having been driven extinct by humans since the late Pleistocene, principally by over-
19 exploitation and habitat destruction, a rate estimated to be 1000 times higher than pre-Anthropocene
20 (natural) background extinction rates (De Vos et al., 2015). Therefore, this level—10%—of species
21 becoming endangered (sensu IUCN) due to loss of suitable climate space (Figure 2.8b), is used here as a
22 threshold moving risk to biodiversity from moderate to high, and twice that (20%) as the threshold from high
23 to very high.
24
25 Key risks assessed here are interconnected. Extinction of species is an irreversible impact of climate change,
26 has negative consequences on ecosystem integrity and functioning, and risk increases steeply with even
27 small rises in global temperature (Section 2.5.1.3; Figure 2.6; Figure 2.7; Figure 2.8). Continued climate
28 change substantially increases risks of carbon losses due to wildfires, tree mortality from drought and insect
29 pest outbreaks, peatland drying, permafrost thaw, and ecosystem structure change which could exacerbate
30 self-reinforcing feedbacks between emissions from high-carbon ecosystems and increasing global
31 temperatures (medium confidence). Thawing of Arctic permafrost alone could release 11-200 Gt carbon
32 (medium confidence). Complex interactions of climate change, land use change, carbon dioxide fluxes, and
33 vegetation changes will regulate the future carbon balance of the biosphere, processes incompletely
34 represented in earth system models. The exact timing and magnitude of climate-biosphere feedbacks and
35 potential tipping points of carbon loss are characterized by high ranges of the estimates, yet studies indicate
36 increased ecosystem carbon losses could cause extreme future temperature increases (medium confidence)
37 (Sections 2.5.2.7; 2.5.2.8; 2.5.2.9; 2.5.3.2; 2.5.3.3; 2.5.3.4; 2.5.3.5; Figure 2.10; Figure 2.11; Table 2.4;
38 Table 2.5; Table 2.S.2; Table 2.S.4)
39
40
41 Table 2.5: Key risks to terrestrial and freshwater ecosystems from climate change. This IPCC chapter assesses these as
42 the most fundamental risks of climate change to ecosystem integrity and the ecosystem services that support human
43 well-being. Climate factors include the primary variables governing the risk. Non-climate factors include other
44 phenomena that can dominate or contribute to the risk. Detection and attribution comprise cases of observed changes
45 attributed predominantly or in part to anthropogenic climate change (Section 2.4.2, 2.4.3, 2.4.4, 2.4.5, Table 2.2, Table
46 2.3, Table 2.S.1). Adaptation includes options to address the risk (Section 2.6). Risk transitions (defined in Figure 2.11)
47 indicate an approximate global mean surface temperature increase, relative to the pre-industrial period (1850-1900), to
48 move from one level of risk to the other and confidence in the assessment. Table 2.S.4 provides details of the
49 temperature levels for risk transitions. Both tables provides details for the key risk burning embers diagram (Figure
50 2.11).
Biodiversity Risk: Increasing high extinction risk (species projected loss of >50% of range) among increasing
number of plant and animal species. The transition from non-detectable to moderate was based on the number of
local population extinctions, major declines of sub-species and two global species extinctions and that are
attributable to climate change. The transition from moderate to high is centred around 1.5°C based on a few taxa
that are known from their basic biology and habitat requirements to be at risk of extinction (endangered) at 1.5°C
and on the increasing number of taxa that are projected to have high extinction risk (losing >50% of their suitable
climate space) affecting >10% of the species in that taxa (1000x natural background rates of extinction). The
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transition to very high comes from the increasing number of taxa projected to have >20% of species at high risk
of extinction. In the worst-case scenario (10th percentile of the models), some of the taxa show >50% of the
species at high risk of extinction. These assessments are also weighted by role the species in the taxa play in
performing ecosystem services (both to the ecosystems and to humans, e.g. pollinators, detritivores). Confidence
for the moderate threshold is high because it is based on observed trends attributed to climate change. Confidence
for future projections are is medium as these are based on one large study (covering more than 135,000 species)
and primarily based on loss of suitable climate. Based on Sections 2.4.2, 2.5.1, 2.6.1, 2.6.6, Table 2.3, Figure 2.6,
Table SM2.1 and Table SM2.2.
Climate factors Non-climate Detection and Adaptation Risk transitions
factors attribution (confidence)
Shifts in geographic land use Observed D&A: Habitat restoration, 0.8ºC undetectable-
placements of climate change, habitat many cases of habitat creation, moderate (high
space; loss of climate degradation population increased connectivity confidence)
space globally; emergence from pollution, extinctions; two of habitats and 1.58°C moderate-
of non-analogue climates, fertilisation, cases of species protected areas, high (medium
increases in extreme invasive extinctions increase in protected confidence)
climate events species (2.4.2.2); species areas, assisted 2.07°C high-very
have tracked their colonisation high (medium
climate niches confidence)
raising
confidence in
SDM projections
(2.4.2.1, 2.4.2.3,
2.4.2.5)
Wildfire: Increasing risk of wildfire that exceeds natural levels, damaging ecosystems, increasing illnesses and
death of people, and increasing carbon emissions. Field evidence shows that anthropogenic climate change has
increased the area burned by wildfire above natural levels across western North America in the period
1984-2017, increasing burned area up to 11 times in one extreme year and doubling burned area over natural
levels in a 32-year period. Burned area has increased in the Amazon, the Arctic, Australia, and parts of Africa and
Asia, consistent with, but not formally attributed to anthropogenic climate change. These changes have occurred
at global mean surface temperature increases of 0.6-0.9ºC. Empirical and dynamic global vegetation models
project increases in burned area and fire frequency above natural levels on all continents under continued climate
change, emergence of an anthropogenic signal from natural variation in fire weather for a third of global area, and
increases of burned area in regions where fire had been rare or absent, particularly Arctic tundra and Amazon
rainforest, at global temperature increases of 1.5-2.5ºC. Models project up to a doubling of burned area globally
and wildfire-induced conversion of up to half the area of Amazon rainforest to grassland at temperature increases
of 3-4.5ºC. (Sections 2.4.4.2, 2.5.3.2)
Climate factors Non-climate Detection and Adaptation Risk transitions
factors attribution (confidence)
Increase in magnitude and Deforestation, Increased burned Reduce deforestation, 0.75°C
duration of high agricultural area in western reduce use of fire in undetectable-
temperatures, decrease in burning, North America tropical forests, use moderate (high)
precipitation, decrease in peatland above natural prescribed burning and 2.0 °C moderate-
relative humidity burning levels allow naturally ignited high (medium)
fires to burn in 4.0°C high-very
targeted areas to high (medium)
reduce fuel loads,
encourage settlement
in non-fire-prone areas
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Tree mortality: Tree mortality that exceeds natural levels degrades habitat for plant and animal species, increases
carbon emissions, and reduces water supplies for people. Anthropogenic climate change caused three cases of
drought-induced tree mortality in the period 1945-2007 in western North America, the African Sahel, and North
Africa in temperate and tropical ecosystems. Pest infestations and wildfire due to climate change also caused
much of the tree mortality in North America. These changes occurred at global mean surface temperature
increases of 0.3-0.9ºC above the pre-industrial period. Models project increasingly extensive drought-induced
tree mortality at continued temperature increases of 1-2ºC. Models project risks of mortality of up to half of
forest area in different biomes at temperature increases of 2.5-4.5ºC. In Amazon rainforests, insufficient plant
moisture reserves during drought increase the risk of tree mortality and, combined with increased fire from
climate change and deforestation, the risk of a tipping point of massive forest dieback and a biome shift to
grassland. (Sections 2.4.4.3, 2.5.2.6, 2.5.3.3, 2.5.3.5)
Climate factors Non-climate Detection and Adaptation Risk transitions
factors attribution (confidence)
Increase in temperature, Deforestation, Tree mortality up Reduce deforestation, 0.6°C undetectable-
decrease in precipitation, land-use to 20% in three reduce habitat moderate (high)
increase in aridity, change regions in Africa fragmentation, 1.5°C: moderate-
increase in frequency and and North encourage natural high (medium)
severity of drought America regeneration, restore 3.0°C high-very
fragmented habitats high (medium)
Ecosystem carbon loss: Increasing risk of ecosystem carbon losses that could substantially raise the atmospheric
carbon dioxide level. Measurements have detected emissions of carbon from boreal, temperate, and tropical
ecosystems in places where increases in wildfire and tree mortality have been attributed to anthropogenic climate
change, at global mean surface temperature increases of 0.6-0.9ºC above the pre-industrial period. Many factors
govern the carbon balance of ecosystems, so changes have not been attributed to climate change. Tropical forests
and Arctic permafrost contain the highest ecosystem stocks of aboveground and belowground carbon,
respectively. Primary tropical forests currently emit more carbon to the atmosphere than they remove due to
deforestation and forest degradation. Wildfires in the Arctic are contributing to permafrost thaw and soil carbon
release. An emissions scenario of 2ºC increase could thaw ~15% of permafrost area and emit 20-100 Gt carbon by
2100, Under emissions scenarios of a 4ºC global temperature increase, models project possible tipping points of
conversion of half of Amazon rainforest to grasslands and thawing of Arctic permafrost that could release 11-200
Gt carbon that could substantially exacerbate climate change. (Sections 2.4.3, 2.4.4.3, 2.4.4.4, 2.5.2.7–10; 2.5.3.2–
5; Figure 2.9; Figure 2.10; Figure 2.11; Table 2.4; Table 2.5; Table 2.S.2; Table 2.S.3; Table 2.S.4)
Climate factors Non-climate Detection and Adaptation Risk transitions
factors attribution (confidence)
Increase in temperature, Deforestation, Losses of carbon Reduce deforestation, 0.75°C
increase in aridity, road and detected in especially in tropical undetectable-
increase in frequency and infrastructure boreal, temperate, forests, reduce road moderate (medium)
severity of drought expansion, and tropical and infrastructure 2°C: moderate-high
agricultural ecosystems, due expansion, especially (medium)
expansion to wildfire and in the Arctic, reduce 4°C high-very high
tree mortality, not use of fire to clear (low)
formally agricultural land,
attributed to increase protected
climate change areas
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Ecosystem Structure Change: Increasing risk of large-scale changes in ecosystem structure. Ecosystem
structural change with most information derived for tropical forest, boreal forest, savannas, and tundra for both
observations and future projections. The transition from non-detectable to moderate is based on detected changes
attributable to climate change, or interactions between changing disturbance regime, climate and rising CO2,
already observed at 0.5°C above pre-industrial, with shifts initially detected in boreal forests, tundra, and tropical
grassy ecosystems. Transition from moderate to high is centred around 1.5°C based on widespread global
observations (at current GSAT of 1.09°C above pre-industrial) that agree with projected future impacts with at
least 10% area of key ecosystems being affected (Box 2.1). Overall confidence in projections is medium, based on
existing observations and projections giving high confidence of risk for several ecosystems but because data and
projections are not available for all biomes, overall confidence lowers to medium. Transition from high to very
high occurs when more than 50% of multiple ecosystems are projected to experience shifts in structure. (Sections
2.4.2.3, 2.4.3, 2.4.5, 2.5.2, Box 2.1, Figure Box 2.1.1, Table Box 2.1.1, Table 2.S.2, Table 2.S.2, Table 2.S.3,
Table 2.S.5)
Climate factors Non-climate Detection and Adaptation Risk transitions
factors attribution (confidence)
Increases in average and Land use Individual Conservation of 0.5°C undetectable-
extreme temperatures, change, species ranges potential refugia, moderate (high)
changes in precipitation livestock shifts, biome habitat restoration, 1.5°C moderate-
amount and timing, grazing, shifts increasing high (medium)
increased atmospheric deforestation, connectivity of 2.5°C high-very
CO2 fire habitats and protected high (medium)
suppression, areas, increase in
loss of native protected areas,
herbivores, changes in grazing and
food, fiber, or fire management
wood
production
1
2
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1
2 Figure 2.11: Key risks to terrestrial and freshwater ecosystems from climate change. This IPCC chapter assesses these
3 as the most fundamental risks of climate change to ecosystem integrity and the ecosystem services that support human
4 well-being, based on observed impacts and future risks of: (far left) Losses of animal and plant species from different
5 ecosystems globally with resulting declines in ecosystem integrity, functioning and resilience (Section 2.4.2.1, 2.4.2.2,
6 2.5.1.3.3); (middle left) wildfire exceeding natural levels (Section 2.4.4.2, 2.5.3.2); (middle) tree mortality exceeding
7 natural levels (Chapter 2.4.4.3, 2.5.3.3), (middle right) ecosystem carbon losses that could occur abruptly and
8 substantially raise atmospheric carbon dioxide (Sections 2.4.3.6—2.4.3.9, 2.4.4.4, 2.5.2.6—2.5.2.10, 2.5.3.4, 2.5.3.5);
9 (far right) major changes occurring in ecosystem structure (Sections 2.4.3, Box 2.1, 2.5.2, Figure 2.9, Figure Box 2.1.1,
10 Table Box 2.1.1, Table 2.S.5). This burning embers diagram shows impacts and risks in relation to changes in global
11 mean surface temperature, relative to the pre-industrial period (1850-1900). Risk levels reflect current levels of
12 adaptation and do not include more interventions that could lower risk. The compound effects of climate change
13 combined with deforestation, agricultural expansion, urbanisation, air, water, and soil pollution, and other non-climate
14 hazards could increase risks. Tables 2.5 and 2.S.4 provide details of the key risks and temperature levels for the risk
15 transitions.
16
17
18 [START FAQ2.4 HERE]
19
20 FAQ2.4: How does nature benefit human health and well-being and how does climate change affect
21 this?
22
23 Human health and well-being are highly dependent on the “health” of nature. Nature provides material and
24 economic services that are essential for human health and productive livelihoods, but studies also show that
25 being in “direct contact with natural environments” has direct positive effects on well-being, health and
26 socio-cognitive abilities. Therefore, the loss of species and biodiversity under climate change will reduce
27 natural space, decrease biodiversity and in turn, decrease human-well-being and health worldwide.
28
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1 Human health and well-being are highly dependent on the “health” of nature. Biodiversity – the variety of
2 genes, species, communities and ecosystems – provides services that are essential for human health and
3 productive livelihoods, such as breathable air, drinkable water, productive oceans and fertile soils for
4 growing food and fuels. Natural ecosystems also help store carbon and regulate climate, floods, disease,
5 pollution and water quality. The loss of species, leading to reduced biodiversity, has direct and measurable
6 negative effects on all of these essential services, and therefore, on humankind. A recent demonstration of
7 this is the decline of pollinator species, with potential negative effects on crop pollination, a fundamental
8 ecosystem function crucial for agriculture. The loss of wild relatives of the domesticated varieties humans
9 rely on for agriculture reduces the genetic variability that may be needed to support the adaptation of crops
10 to future environmental and social challenges.
11
12 The number of species that can be lost before negative impacts occur is not known and is likely to differ in
13 different systems. However, in general, more diverse systems are more resilient to disturbances and able to
14 recover from extreme events more quickly. Biodiversity loss means there are fewer connections within an
15 ecosystem. A simpler food web with fewer interactions means less redundancy in the system, reducing the
16 stability and ability of plants and animal communities to recover from disturbances and extreme weather
17 events such as floods and drought.
18
19 In addition to “material” and economic services such as eco-tourism, nature also provides cultural services
20 such as recreation, spirituality and well-being. Specifically, being in “direct contact with natural
21 environments” (versus urban environment) has a high positive and causal impact on human well-being (e.g.
22 mood, happiness), psychological and physical health (energy, vitality, heart rate, depression) and socio-
23 cognitive abilities (attention, memory, hyperactivity, altruism, cooperation). Therefore, the loss of species
24 and biodiversity under climate change and urbanisation will reduce natural space, decrease biodiversity and
25 in turn, decrease human-well-being and health worldwide.
26
27 Finally, the extent to which humans consider themselves as part of the natural world – known as human-
28 nature connectedness – has been demonstrated to be closely associated with human health and well-being.
29 Individuals who are more connected to nature are not only happier and healthier but also tend to engage
30 more in pro-nature behaviours, making the enhancement of human-nature connectedness worldwide a
31 valuable win-win solution for humans and nature to face environmental challenges.
32
33
34
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1 Figure FAQ2.4.1: Positive relationship between human health and well-being and nature conservation. Nature
2 provides essential services to humans including material and economic services (i.e. ecosystem services) as well as
3 cultural, experiential and recreational services, which, in turn enhance human psychological and physical health, and
4 well-being. People who are more connected to nature are not only happier and healthier but are also more likely to
5 engage in pro-nature behaviours, making the enhancement of human-nature connectedness worldwide a valuable win-
6 win solution for humans and nature to face environmental challenges.
7
8 [END FAQ2.4 HERE]
9
10
11 2.6 Climate Change Adaptation for Terrestrial and Freshwater Ecosystems
12
13 Adaptation to reduce vulnerability of ecosystems and their services to climate change has been addressed in
14 previous IPCC Reports, with AR4 and AR5 recognising both autonomous adaptation and human assisted
15 adaptation to protect natural species and ecosystems. In AR5, Ecosystem-based Adaptation (EbA),
16 adaptation for people, based on better protection, restoration and management of the natural environment,
17 was identified as an area of emerging opportunity, with a dedicated Cross Chapter Box on the topic. In the
18 SRCCL report, conservation, EbA and related concepts were integrated throughout the report; SR1.5 also
19 noted the role of EbA. Since the last assessment report the scientific literature has expanded considerably,
20 with growing interest in the concept of Nature-based Solutions (NbS). This section assesses this new
21 literature and its implications for the implementation of climate change adaptation.
22
23 Previous sections of this chapter have set out the vulnerability of natural and semi-natural ecosystems to
24 climate change and the risks this poses both to biodiversity and ecosystem services (also sometimes
25 described as ‘Nature’s Contributions to People’). Natural systems respond to climatic and other
26 environmental changes in variety of ways. Individual organisms can respond through growth, movement
27 and developmental processes. Species and populations genetically adapt to changing conditions and evolve
28 over successive generations. Geomorphological features, such as the path of watercourses, can also change
29 naturally in response to climate change. However, there is a limit to which these natural processes can
30 maintain biodiversity and the benefits people derive from nature, partly because of intrinsic limits, but also
31 because of the pressures that people exert on the natural environment. Most of this section therefore focuses
32 on human interventions to build the resilience of ecosystems, enable species to survive or to adjust
33 management to climate change. Vulnerability is in many cases exacerbated by the degraded state of many
34 ecosystems as a result of human exploitation and land use change, leading to fragmentation of habitats, loss
35 of species and impaired ecosystem function. This interaction between climate change and environmental
36 degradation means that protecting ecosystems in a natural or near-natural state will be an important pre-
37 requisite for maintaining resilience and give many species the best chance of persisting in a changed climate
38 (Belote et al., 2017; Arneth et al., 2020; Ferrier et al., 2020; França et al., 2020). Protection from
39 degradation, deforestation and exploitation is also essential to maintain critical ecosystem services, including
40 carbon storage and sequestration and water supply (Dinerstein et al., 2020; Pörtner, 2021).
41
42 It is worth briefly considering some key concepts that are relevant to adaptation in ecosystems. Adaptation
43 for biodiversity and ecosystems can encompass both managing change and building resilience. We use the
44 definition of ‘resilience’ set out Chapter 1: ‘the capacity of social, economic and environmental systems to
45 cope with a hazardous event or trend or disturbance, responding or reorganising in ways that maintain their
46 essential function, identity and structure while also maintaining the capacity for adaptation, learning and
47 transformation’, It includes the concept of ‘resistance’, which is used in some ecological literature to
48 distinguish systems which a resistant to change from those that recover quickly from change. We consider
49 both interventions designed primarily to protect biodiversity and those intended to reduce the risks of climate
50 change to people.
51
52 A variety of terms are used to describe using environmental management reduce the impacts of climate
53 change on people in ways that also benefit biodiversity in the scientific literature, particularly Ecosystem
54 based Adaptation (EbA) and Nature-based Solutions (NbS) (see also Section 1.4). EbA is the use of
55 biodiversity and ecosystem services as part of an overall adaptation strategy to help people to adapt to
56 climate change (CBD, 2009). EbA aims to maintain and increase the resilience and reduce the vulnerability
57 of ecosystems and people in the face of the adverse effects of climate change (Vignola et al., 2009). NbS is a
58 broader term which is not restricted to climate change and is also often used to refer to climate change
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1 mitigation; it has been defined by the International Union for the Conservation of Nature (IUCN) as ‘Actions
2 to protect, sustainably manage and restore natural or modified ecosystems that address societal challenges
3 effectively and adaptively, simultaneously providing human well-being and biodiversity benefits’ (Cohen-
4 Shacham et al., 2016). This widely accepted definition excludes actions, which use the natural environment
5 to solve human problems but do not provide benefits for biodiversity and is closely linked to the concept of
6 the Ecosystem Approach. NbS is not a universally accepted term but it is increasingly used in the scientific
7 literature. It is a concept which recognises the importance of biodiversity in ecosystem service provision and
8 offers the opportunity to address climate change and loss of biodiversity together in an efficient integrated
9 way (Chong, 2014; Seddon et al., 2020a; Ortiz et al., 2021). Given the focus of this chapter is on adaptation
10 we primary use the term EbA as it is more specific, but we do so understanding that it can be regarded as a
11 subset of NbS. The wider concept of NbS for climate change adaptation and mitigation is covered in a Cross-
12 Chapter Box on the topic (see Cross-Chapter Box NATURAL this Chapter).
13
14 Whilst we distinguish between adaptation for biodiversity and EbA, it is important to recognise that the two
15 are linked in that if ecosystems themselves are not resilient to climate change, they will not be able to
16 provide adaptation benefits for people. The case for resourcing biodiversity conservation and building the
17 resilience of ecosystems is also strengthened when there are direct benefits for people in addition to the more
18 general benefits of biodiversity.
19
20 Ecosystems are specifically included in the adaptation goals set out in the Paris Agreement and are addressed
21 in most national adaptation plans (Seddon et al., 2020b). There is also now a large number of adaptation
22 programmes and plans for local governments and governmental and non-governmental conservation
23 organisations. Adaptation for and by ecosystems needs to be understood and developed in the wider contexts
24 of conservation, Climate Resilient Development and Sustainable Development: there is significant potential
25 synergies, but also conflicts between different objectives, which require an integrated approach (covered
26 further in 2.6.7).
27
28 2.6.1 Limits to Autonomous (Natural) Adaptation
29
30 Natural ecosystems often have a high degree of resilience and can to some extent adjust to change. Species
31 can adjust through evolutionary adaptation, distribution change, behavioural change, developmental
32 plasticity and ecophysiological adjustment. There are, however, limits to autonomous adaptation, because of
33 intrinsic limitations, the rate at which the climate is changing and the degraded state of many ecosystems.
34
35 None of the evolutionary changes either documented or theorised would enable a species to survive and
36 reproduce in climate spaces that it does not already inhabit. Evolutionary responses are very unlikely to
37 prevent species extinctions in the case of that species losing its climate space entirely on a regional or global
38 scale (Parmesan and Hanley, 2015). At highest risk are the world's most cold-adapted species (whose
39 habitats are restricted to polar and high mountaintop areas). Examples include the polar bear (Regehr et al.,
40 2016), "sky-island" plants in the tropics (Kidane et al., 2019), mountain-top amphibians in Spain (Enriquez-
41 Urzelai et al., 2019), mountain-top lichens in the Appalachians (USA) (Allen and Lendemer, 2016), and
42 silverswords in Hawaii (Krushelnycky et al., 2013). However, there is potential for using evolutionary
43 changes to enhance the adaptive capacity of target species, as is being done in the Great Barrier reef by
44 translocating symbionts and corals that have survived recent intense heat-induced bleaching events into areas
45 that have had large die-off (Rinkevich, 2019). Multiple studies assessed when and how evolution might be
46 able to help wild species adapt to climate change (Ratnam et al., 2011; Sgro et al., 2011).
47
48 Some of the reasons cited in the literature as limits to autonomous adaptation are:
49
50 1) Genetic changes in populations require many generations and for many species operate on longer time
51 scales than those, on which the climate is currently changing.
52
53 2) Many species are moving to higher latitudes as the climate warms, but not all are keeping pace with
54 changes in suitable climate space (Valladares et al., 2014; Mason et al., 2015). Such climate debt indicates
55 an inability for non-genetic autonomous adaptation (e.g. evidence-limited ability for plastic responses, such
56 as stemming from dispersal limitations, or behavioural restrictions, or physiological constraints).
57
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1 3) Some species have low capacity for dispersal, which, combined with increased fragmentation of habitats,
2 creates barriers to range shifts to match climate warming. Studies have shown that changes in distribution of
3 species and composition of communities are limited by the presence of intensively managed agricultural land
4 fragmenting natural habitats (Oliver et al., 2017).
5
6 There are a variety of mechanisms which promote the resilience of ecosystems through persistence,
7 recovery, and reorganisation (Falk et al., 2019). Changes in the balance of different plant species within a
8 community can maintain the persistence of the community itself, maintaining its value as a habitat for other
9 species and providing ecosystem services (add reference?). In some cases there are negative feedback
10 mechanisms between biological and physical processes, for example in peatlands, lowered water tables
11 resulting from drier conditions can lead to reduced permeability of peat, increasing rates of water loss (Page
12 and Baird, 2016). There are limits to this resilience and the concept of tipping points beyond which
13 ecosystems change state and returning to the original state has been subject of much recent research (van Nes
14 et al., 2016). There is clear evidence that the degradation of ecosystems has reduced their resilience and
15 restoration can help to reduce risks to biodiversity and ecosystem services, discussed below (see Section
16 2.6.2, 2.6.3). However, as rates of climate change increase, the limits of this approach will start to be
17 reached and losses, including some with potentially catastrophic consequences, cannot be prevented; this is
18 discussed further in Section 2.6.6.
19
20 2.6.2 Adaptation for Biodiversity Conservation
21
22 A variety of approaches have been identified as potential adaptation measures which people can take to
23 reduce the risks of climate change to biodiversity. (Heller and Zavaleta, 2009 ) (quoted in AR5) identified
24 113 categories of recommendation for adaptation from a survey of 112 papers and reports. Since this time
25 the literature has greatly expanded, with thousands of relevant publications. Whilst there is increasing
26 interest in adaptation for biodiversity conservation and a wide range of plans and strategies, there is less
27 evidence of these plans being implemented. Since AR5 a number of studies, predominantly from Europe and
28 North America, have investigated the extent to which adaptation has been integrated into conservation
29 planning and is being implemented at site and regional scale (Macgregor and van Dijk, 2014; Delach et al.,
30 2019; Prober et al., 2019; Clifford et al., 2020; Barr et al., 2021; Duffield et al., 2021). A common pattern in
31 these studies is that vulnerability has been assessed and potential adaptation actions identified, but
32 implementation has been limited beyond actions to improve ecological condition, which may increase
33 resilience at a local scale.
34
35 To date most scientific literature on adaptation to reduce risk to biodiversity from climate change has been
36 based on ecological theory rather than observations or practical experience. A recent review (Prober et al.,
37 2019) concluded that out of 473 papers on adaptation, only 16% presented new empirical evidence and very
38 few assessed the effectiveness of actual adaptation actions. It is also the case that relatively little research is
39 focussed on local-scale management interventions rather than larger scale strategies (Ledee et al., 2021),
40 although there are some exceptions (Duffield et al., 2021).
41
42 Although direct assessments of the effectiveness of adaptation actions are rare, since AR5 there has been an
43 increasing number of empirical analyses of how different land use and management influences the
44 vulnerability of species and habitats. As climate change often interacts with other factors including
45 ecosystem degradation and fragmentation (Oliver et al., 2015a), actions to address these other interacting
46 factors is expected to build resilience to climate change. Table 2.6 summarises evidence that supports the
47 main categories of proposed adaptation measures. We have taken an inclusive approach and included studies
48 that address extreme weather events such as droughts, which may be exacerbated by climate change as well
49 as long-term changes in climate variables. We have not distinguished between studies in which climate
50 change adaptation was an explicit focus and those in which lessons for adaptation can be learnt from studies
51 conducted for other reasons but inform the assessment of the impacts of actions identified as potential
52 adaptation measures.
53
54
55 Table 2.6: Evidence to support proposed climate change adaptation measures for biodiversity. Highlights that
56 adaptation for biodiversity is a broad concept, encompassing a wide range of actions. It includes targeted interventions
57 to change the microclimate for particular species (for example by shading); through to changing national conservation
58 objectives to take account of changing distributions of species and communities. It includes targeted actions addressing
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1 both climate change and protection and restoration of ecosystems, with multiple additional benefits including reducing
2 vulnerability to climate change. Most of the studies are not direct tests of the impacts of adaptation actions, which, as
3 noted above, is an important evidence gap. There is also a major limitation in that reported studies are predominantly
4 from Europe, North America and Australasia, with little research in other regions.
Proposed Adaptation Confidence Comment Selected References
Measures for Assessment
Biodiversity
Protect large areas of Robust Considerable evidence that intact systems Dinerstein et al. (2019);
natural and semi- evidence, high provide better quality and quantity of Woodley et al. (2019);
natural habitat agreement ecosystem services; that larger intact areas Brooks et al. (2020);
provide better ecosystem services; that Zhao et al. (2020); Sala
species' extinction risk with disturbances, et al. (2021); Pimm et al.
including climate change, are reduced by (2018); Hannah et al.
having large, connected populations; that (2020); Luther et al.
more biodiverse systems provide higher
(2020)
levels of ecosystem services and are more
resilient to climate change than degraded
systems that have lost species
Increase connectivity in Medium Good evidence that some species move more Keeley et al. (2018);
terrestrial habitats – evidence, quickly in more connected landscapes. Stralberg et al. (2019);
corridors, stepping Medium However, not all species do and some von Holle et al. (2020)
stones agreement species that benefit are invasive / pest /
disease species and there is limited empirical
evidence showing connectivity has reduced
climate change impacts on species to date.
Increase connectivity in Limited Connectivity is needed to maintain species Hermoso et al. (2016);
river networks evidence, and population movements, but river reaches Thieme et al. (2016);
High and catchments lack integrated protection. Abell et al. (2017);
agreement Brooks et al. (2018)
Increase habitat patch Limited Generally increase resilience because of Eigenbrod et al. (2015);
size site and expand evidence functioning natural processes, large species Oliver et al. (2015a)
protected areas High populations and refugial areas
agreement
Increase replication Limited Various benefits inferred, including, wider Mawdsley et al. (2009);
and representation of evidence, range of climatic and other conditions, less Thomas et al. (2012a);
protected areas High risk of extreme events affecting many rather Virkkala et al. (2014);
agreement than few areas. More sites available for Gillingham et al. (2015);
colonisation by range expanding species and Pavón-Jordán et al.
better conditions to maintain species in situ (2020b)
under range contraction.
Protect microclimatic Medium Locally cool areas can be identified and Haslem et al. (2015);
refugia evidence, there is evidence species can survive better Suggitt et al. (2015);
High in such areas. Isaak et al. (2016);
agreement Morelli et al. (2016);
Merriam et al. (2017);
Bramer et al. (2018);
Suggitt et al. (2018);
Massimino et al. (2020)
Creating shade to Limited Creating shade has been used as an Broadmeadow et al.
lower temperatures for evidence, adaptation strategy, for example by (2011); Lagarde et al.
vulnerable species High watercourses but improvements in species (2012); Patino-Martinez
agreement survival under warming conditions have yet et al. (2012); Thomas et
to be demonstrated. al. (2016)
Restoring hydrological Medium Wetland restoration is well established as a Carroll et al. (2011);
processes of wetlands, evidence, conservation measure in some countries. Can Hossack et al. (2013);
rivers and catchments, High reduce vulnerability to drought with climate Dokulil (2016);
including by raising agreement change but evidence to demonstrate Timpane-Padgham et al.
water tables and effectiveness as an adaptation measure is (2017); Moomaw et al.
restoring original limited and requires long-term monitoring of (2018)
channels of a range of sites. Little restoration of
watercourses, degraded tropical peatlands to date
Restoration of natural Medium Includes reintroduction of native herbivores Coffman et al. (2014);
vegetation dynamics evidence, and reversing woody encroachment of Valkó et al. (2014);
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Medium savannas. Benefits for biodiversity are well Batáry et al. (2015);
agreement established in a wide range of different Smit et al. (2016);
regions Stevens et al. (2016);
Hempson et al. (2017);
Bakker and Svenning
(2018); Cromsigt et al.
(2018); Fulbright et al.
(2018); Olofsson and
Post (2018)
Reduce non-climatic Limited As a general principal climate change is Oliver et al. (2017);
stressors to increase evidence, recognised as a ‘threat multiplier’ but Pearce-Higgins et al.
resilience of ecosystems Medium specific details are often unclear (2019)
agreement
Assisted translocation Limited Assisted translocation has been commonly Willis et al. (2009);
and migration of evidence, suggested as an adaptation measure, but Brooker et al. (2018);
species Medium there have been very few examples of this Skikne et al. (2020)
agreement being trialed. Translocations have been
carried out for other reasons and lessons for
climate change adaptation have been
inferred
Intensive management Medium A variety of approaches including Angerbjörn et al. (2013);
for specific species Evidence, manipulating microclimate and competition Greenwood et al. (2016);
Medium between species to improve chances of Pearce-Higgins et al.
Agreement survival under climate change. (2019)
Ex-situ conservation Not possible Seed banks have been established but long- Christmas et al. (2016)
(seedbanks/genetic to assess at term effectiveness could only be evaluated at
stores, etc.) present time a later point.
Adjusting conservation Robust Conservation management will need to take Stein et al. (2013);
strategies and site evidence, account of changes that cannot be prevented, Rannow et al. (2014);
objectives to reflect High for example in the distribution of species and Oliver et al. (2016);
changing species Agreement composition of communities, in order to Stralberg et al. (2019);
distributions and protect and manage biodiversity as Duffield et al. (2021)
habitat characteristics effectively as possible in a changing climate.
Softening the matrix of Limited Potential for agri-environment schemes to do Donald and Evans
unsuitable habitats evidence this in hostile farmed landscapes. (2006); Stouffer et al.
between patches to (2011)
increase permeability
for species movement in
response to climate
change
1
2
3 Many climate adaptation actions for biodiversity operate at the landscape scale (von Holle et al., 2020). The
4 total area of habitat, how fragmented it is, the size of habitat patches and the connectivity between them are
5 interlinked properties at this scale. A growing number of studies have investigated how these properties
6 affect species ability to persist in situ and colonise new areas. Overall, larger areas of semi-natural habitat are
7 associated with increased resilience to ongoing climate change and extreme events and the capacity to
8 colonise new areas (Haslem et al., 2015; Oliver et al., 2017; Papanikolaou et al., 2017). Larger habitat
9 patches can support larger populations, which are more likely to maintain themselves and recover from
10 periods of adverse conditions. A large patch size has been found to increase resilience of some populations
11 of species to extreme events such as droughts (Oliver et al., 2015b). They are also more likely to provide a
12 range of different resources and microclimate conditions, which may increase chances of species persistence
13 under climate change. A larger area of habitat may also enables greater connectivity between patches and
14 increases the chances of species colonising new areas as they track climate change. (Oliver et al.,
15 2015b)Protecting and restoring natural processes is a general principle for maintain and building resilience to
16 climate change for biodiversity (Timpane-Padgham et al., 2017). One element of this is ensuring naturally
17 functioning hydrology for wetlands and river systems (Table 6), which is particularly important in a context
18 of changing rainfall patterns and increased evapo-transpiration. An important development in approaches to
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1 conservation over recent decades has been the concept of re-wilding (Schulte To Bühne et al., 2021); this
2 encompasses a number of elements of restoring natural processes, including the reintroduction of top
3 predators, larger conservation areas and less prescriptive outcomes than much previous conservation. There
4 are elements of re-wilding which may well contribute to building resilience to climate change but it will be
5 increasingly important to factor climate change adaptation into the planning of rewilding schemes (Carroll
6 and Noss, 2021).
7
8 The most consistently cited climate change adaptation measure for species is increasing connectivity to
9 facilitate colonisation of new areas. This reflects the fact that many species’ habitats are highly fragmented
10 in areas with more intensive land management, which prevents them naturally changing their range to track
11 changing climatic conditions. Advances and innovations in modelling techniques can support decision
12 making on connectivity (Littlefield et al., 2019). There is evidence from empirical as well as modelling
13 studies that species can disperse more effectively in better connected areas in terrestrial habitats (Keeley et
14 al., 2018). The issues are different in more natural landscapes—species may still be threatened in
15 intrinsically isolated habitats, such as mountain top, but connectivity cannot be created in the same way.
16 Evidence suggests that increased connectivity will only benefit a subset of species, probably those which are
17 intermediate habitat specialists that are able to disperse (Pearce-Higgins et al., 2014). Generalists do not
18 require corridors or stepping stones whilst many corridors or stepping stones will not be of sufficient quality
19 to be used by the most habitat specialists. There should also be a caveat to the general principle that
20 increasing connectivity is a benefit for climate change adaptation. It can increase the spread of invasive, pest
21 and disease-causing species into newly suitable regions. In some places isolated refugia may better allow
22 vulnerable species and biological communities to survive.
23
24 There are many different approaches to increasing connectivity, ranging from increasing overall area of
25 suitable habitat through to ‘corridors’ and ‘stepping stones’, with different strategies likely to be more
26 effective for different species and circumstances (Keeley et al., 2018). Connectivity can also be important in
27 increasing resilience of populations to extreme climatic events (Newson et al., 2014; Oliver et al., 2015b).
28 Within freshwater environments, connectivity of watercourses is essential. Fluvial corridors are necessary to
29 ensure migrating fish population survival, even without climate change; with climate change, connectivity
30 becomes crucial for relatively cold-adapted organisms to migrate upstream to colder areas. Connectivity is
31 also important for the larvae of benthic invertebrates to be able to drift downstream and hence to disperse
32 (Brooks et al., 2018); for adult benthic invertebrates, riparian and terrestrial habitat features can potentially
33 affect dispersal. Connectivity within river and wetland systems for some species can also mediated by more
34 mobile animal species such as fish and birds (Martín-Vélez et al., 2020) Which factors are the most
35 important in either promoting their colonisation of new sites or persisting in situ will differ between species
36 and locations. Some general principle have been recognised and can guide conservation policy and practice
37 (England and RSPB, 2020; Stralberg et al., 2020) but this will often require additional investigation and
38 planning based on understanding individual the niche of specific species.
39
40 Managed, translocation by moving species from areas where the climate is becoming unsuitable to places
41 where there persistence under climate change is more likely has been discussed as an adaptation option for
42 many years. So far there have been very few examples of this and it is likely to be a last resort in most cases
43 as in many cases it requires a large investment of resources, the outcome is uncertain and there may be
44 adverse impacts on receiving sites. Nevertheless there are cases where it may be a be a viable option
45 (Stralberg et al., 2019). This is discussed in more detail as a case study in section 2.6.5.1.
46
47 The evidence that species can persist in microclimatic refugia where suitable conditions for them are
48 maintained locally (for example, because of variations in topography) has increased in recent years. This has
49 opened up the potential to include refugia in conservation plans and strategies to facilitate local survival of
50 species (Jones et al., 2016; Morelli et al., 2016; Morelli et al., 2020). For example, in targeting management
51 actions (Sweet et al., 2019) aimed at supporting populations of species and is likely to become an important
52 aspect of climate change adaptation for biodiversity conservation in future. It is also possible to manipulate
53 microclimate for example by creating shelters for birds’ nests (see case study of African Penguins 2.6.5.5)
54 (Patino-Martinez et al., 2012). One specific approach of this sort is planting or retention of trees and wooded
55 corridors to shade water courses (see also case study 2.6.4.5 below; Thomas et al., 2016). In the latter case,
56 riparian shading can also possibly help to reduce phytoplankton and benthic diatom growth in smaller
57 streams and rivers (Halliday et al., 2016).
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1
2 Refugia often refer to locally places in a landscape, such as on shaded slopes or high elevations, but they can
3 also include places where water supply may continue during dry periods (Morelli et al., 2016) Monitoring
4 can reveal which streams, wetlands, springs, and other aquatic resources retain suitable discharges, water
5 quality, wetland area, and ecological integrity, especially during dry years (Cartwright et al., 2020).
6 Measures to conserve drought refugia may include protecting springs and other groundwater-fed systems
7 from groundwater extraction, contamination, salinisation, surface-water diversion, channelisation of
8 streams, livestock trampling, recreation and invasive species, as well as effects from surrounding landscape
9 disturbances (Cartwright et al., 2020; Krawchuk et al., 2020). Restoration of degraded aquatic ecosystems
10 can include removal of flow-diversion infrastructure, exclusion of livestock, reduction of other human
11 impacts, geomorphic restructuring, invasive species removal, and planting of native riparian vegetation.
12
13 In fire prone areas, fire suppression and management is a key element of protecting refugia ( Section 2.6.5.8
14 below). In ecosystems in which a natural fire regime has been suppressed restoration practices such as
15 prescribed fires, thinning trees, and allowing some wildfires where it benefits the ecosystem can be
16 introduced to reduce increasing risks from severe wildfires (Meigs et al., 2020).
17
18 Protected areas—areas of land set aside for species and habitat protection with legal protection from
19 development or exploitation—have been a cornerstone of nature conservation for many years. Their
20 effectiveness under a changing climate has been the subject of debate and investigation. There is now a large
21 body of evidence demonstrating that colonisations by range shifting species are more likely to occur on
22 protected sites compared to non-protected sites for a wide range of taxa (e.g. Thomas et al., 2012b;
23 Gillingham et al., 2015), including across continents (Pavón-Jordán et al., 2020a). This is probably because
24 by protecting large areas of natural and semi-natural habitats they provide suitable places for colonising
25 species (Hiley et al., 2013) which may not be available in the surrounding landscape. Although the evidence
26 for protected areas being associated with reduced extinctions is weaker, the finding in Gillingham et al.
27 (2015) that protected sites were associated with reduced extinction rates at low latitudes and elevations is
28 strongly suggestive that they can help species' persistence in the face of climate change.
29
30 It is intrinsically difficult to assess the effectiveness of climate change adaptation measures, the benefit of
31 which will be realised in years and decades ahead (Morecroft, 2019, Measuring success). Nevertheless,
32 taking account of the wide range of evidence reported above, including theory, modelling and observations
33 of climate change impacts in contrasting circumstances, it is possible to make an overarching assessment that
34 appropriate adaptation measures can reduce the vulnerability of many aspects of biodiversity to climate
35 change (robust evidence, high agreement). It is also however clear that to be most effective and avoid
36 unintended consequences, measures need to be carefully implemented taking account of specific local
37 circumstances (robust evidence, high agreement) and include the management of inevitable changes (robust
38 evidence, high agreement). It is also clear that whilst there are now many plans and strategies for adapting
39 biodiversity conservation to climate change, many have yet to be implemented fully (medium evidence, high
40 agreement).
41
42 2.6.3 Nature-based Solutions: Ecosystem-based Adaptation
43
44 Ecosystem-based Adaptation is an increasingly important element of Nature-based Solutions (see 2.6 above).
45 A study published in 2020 found that out of 162 Intended Nationally Determined Contributions (covering
46 189 countries) submitted to the United Nations Framework Convention on Climate Change, as commitments
47 to action under the Paris Agreement, 109 indicated ‘ecosystem-orientated visions’ for adaptation, although
48 only 23 use the term ‘Ecosystem-based Adaptation’ (Seddon et al., 2020b).
49
50 EbA includes a range of different approaches. Examples include restoring coastal and river systems to
51 reduce flood risk and improve water quality and the creation of natural areas within urban areas to reduce
52 temperatures through shading and evaporative cooling. EbA is closely linked with a variety of other concepts
53 such as ecosystem services, natural capital and Disaster Risk Reduction (DRR). EbA was becoming a well-
54 recognised concept at the time of AR5 but implementation was still at an early stage in many cases. Since
55 then pilot studies have been assessed and EbA projects have been initiated around the world. The evidence
56 base continues to grow (Table 2.7) and this has led to increasing confidence in approaches which have been
57 shown to work leading to further expansion in some countries (Table 2.7). However, this is not uniform and
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1 there is relatively little synthesis across disciplines and regions (Seddon et al., 2020a). Chausson et al. (2020)
2 used a systematic mapping methodology to characterise 386 published studies. They found that interventions
3 in natural or semi-natural ecosystems ameliorated adverse climate change impacts in 66% of cases, with
4 fewer trade-offs than for more artificial systems such as plantation forest. However, the evidence base has
5 substantial gaps. Most of the evidence has been collected in the Global North and there is a lack of robust,
6 site-specific investigations of the effectiveness of interventions compared to alternatives and of more holistic
7 appraisals accounting for broader social and ecological outcomes.
8
9
10 Table 2.7: Examples of key Ecosystem- based Adaptation measures with assessments of confidence. Note only
11 adaptation related services are shown – many measures also provide a range of other benefits to people. All also
12 provide benefits for biodiversity.
Ecosystem
Climate Social Relevant
Services for
Ecosystem Based Confidence Change Benefits Ecosystems Selected
climate
Adaptation Measures Assessment Impact from and References
change
addressed adaptation contexts
adaptation
Natural Flood risk Medium Flood Increased Reduction of Multiple Iacob et al. (2014);
management in river evidence regulation; rainfall flood Meli et al. (2014);
systems –restoring Medium sediment intensity damage Burgess-Gamble et
natural river courses agreement retention; Increased al. (2017); Dadson
(removing water storage; water et al. (2017);
canalisation), restoring water security Rowiński et al.
and protecting purification (quality and (2018)
wetlands and riparian supply)
vegetation
Shade rivers and Medium Provision of Warmer Food Multiple Broadmeadow et
streams by restoration evidence fish stocks water security al. (2011); Isaak et
of riparian vegetation High temperatures income al. (2015);
or trees. agreement benefits Williams et al.
(2015b); Thomas
et al. (2016)
Managed realignment Robust Coastal storm Rising sea Protection of Coastal Høye et al. (2013);
of coastlines; re- evidence and flood level life, property Spalding et al.
establishing and High protection Increasing and (2014); Narayan et
protecting coastal agreement Coastal storm energy livelihoods al. (2016); Morris
habitats including erosion Water et al. (2018);
mangroves, salt marsh, control security Chowdhury et al.
Salt water (2019); Powell et
intrusion al. (2019)
prevention
Agroforestry and other Medium Local climate High Food Multiple Vignola et al.
agro- evidence regulation; temperature security (2015); Torralba et
ecological/conservation Medium soil or changing income al. (2016); Paul et
agricultural practices agreement conservation; temperature benefits al. (2017); Blaser
on agricultural land soil nutrient regimes et al. (2018);
regulation; Changing Nesper et al.
water precipitation (2019); Verburg et
conservation; regimes al. (2019);
pest control; Aguilera et al.
food (2020); Tamburini
provisioning et al. (2020)
Restore and maintain Robust Local climate Higher Cooler Urban Norton et al.
urban and peri-urban evidence regulation temperatures microclimate areas (2015); Liquete et
green space – trees, High Flood and Reduced al. (2016); Liu
parks, local nature agreement regulation heatwaves flood (2016); Bowler et
reserves, created Water Increased damage, al. (2017); Aram et
wetlands purification rainfall water al. (2019);
Water storage intensity or security
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Erosion reduced Stefanakis (2019);
control rainfall Ziter et al. (2019)
intensity
Ecological restoration Medium Regulation of Mega-fires Reduce Fire- Waldram et al.
for fire risk reduction evidence wildfires from deaths and adapted (2008); Stephens et
through restoration of High increases in infrastructure ecosystems al. (2010); van
natural vegetation and agreement drought and damage from Mantgem et al.
herbivory and by re- heat fires (2016); Boisramé
instating natural fire et al. (2017);
regimes Johnson et al.
(2018); Parisien et
al. (2020a);
Parisien et al.
(2020b); Stephens
et al. (2020)
Invasive non-native Robust Water Increasing water Water van Wilgen and
aquatic plant control to evidence provision droughts security scarce Wannenburgh
improve water security High regions (2016)
agreement prone to an
increase in
droughts
Woody plant control Medium Fodder Elevated income Savanna Haussmann et al.
(of encroaching evidence biomass CO2 through bush and (2016)
biomass) in open Medium production increasing clearing, grasslands
grassy ecosystems to agreement tree growth/ fuelwood
restore and maintain increases in supplies,
grassy vegetation (see rainfall restore
2.4.3.5) promoting grazing
tree growth
Rangeland Medium Fodder Changing Food Rangelands Descheemaeker et
rehabilitation and evidence biomass precipitation security al. (2010); Wairore
management such as Medium production; and Water et al. (2016);
through livestock agreement soil erosion temperature security, Kimiti et al. (2017)
enclosures, appropriate control; soil regimes income
grazing management, formation; including benefits
re-introducing native nutrient prolonged
grassland species cycling; water dry seasons
retention and
increased
drought
frequency
Sustainable forestry of Medium Timber Increased Livelihood Boreal, Gyenge et al.
biodiverse managed evidence production frequency and income temperate, (2011); Barsoum et
forests, maintaining Medium and severity benefits subtropical, al. (2016); Jactel et
forest cover and agreement of storms tropical al. (2017); Cabon
protecting soils Higher forests et al. (2018)
temperatures
Changing
precipitation
regimes
(more
intensive wet
and dry
periods)
Increased
incidents of
wildfire,
pest, and
disease
outbreaks
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Watershed Medium Flood control; Changing Food Boreal, Filoso et al.
reforestation and evidence erosion precipitation security; temperate, (2017);
conservation for Medium control; water regimes Water subtropical, Bonnesoeur et al.
hydrological services agreement provisioning; security; tropical (2019)
water Flood forests
purification Protection
Multifunctional forest Medium Timber and Multiple Food Boreal, Lunga and
management and evidence non-timber security; temperate, Musarurwa (2016);
conservation to provide Medium forest Water subtropical, Strauch et al.
climate resilient agreement production; security; tropical (2016); Adhikari et
sources of food and fuel wood income forests al. (2018)
livelihoods and protect production; benefits
water sources water
provisioning;
water
purification
Slope revegetation for Robust Soil retention; Increased Reduced Montane Fox et al. (2011a);
landslide prevention evidence slope rain landslide and other Krautzer et al.
and erosion control High stabilisation frequency damage; steep (2011); Leal Filho
agreement prevention of sloped et al. (2013);
loss of life regions Bedelian and
Ogutu (2017);
Getzner et al.
(2017); de Jesús
Arce-Mojica et al.
(2019)
1
2
3 Restoring coasts, rivers and wetlands to reduce flood risk have probably seen the largest investment in EbA
4 and it is becoming an increasingly accepted approach in some places (e.g. case studies in Sections 2.6.5.2,
5 2.6.5.7) although significant social, economic and technical barriers remain (Wells et al., 2020; Bark et al.,
6 2021; Hagedoorn et al., 2021). Natural flood management encompasses a wide range of techniques in river
7 systems and at the coast and have been used in varied locations around the world. In tropical and sub-
8 tropical areas, the restoration of mangroves to reduce the risk of coastal flooding is widely advocated,
9 evidence-based approach (for example (Høye et al., 2013; Sierra-Correa and Kintz, 2015; Powell et al.,
10 2019)). In temperate regions salt marsh is a similarly important habitat (Spalding et al., 2014). Both provide
11 buffering against rising sea levels and storm surges. Managed realignment of the coast, by creating new
12 habitats can lead to a loss of terrestrial and freshwater ecosystems, but it can protect them and the services
13 they provide by reducing the risks of catastrophic failure from hard-engineered sea defences. In river
14 systems (Iacob et al., 2014) management of both catchments and the channel itself is important: restoring
15 natural meanders in canalised water courses and allowing the build-up of woody debris can slow flows rates;
16 restoring upstream wetlands or creating them in urban and peri-urban situations can store water during flood
17 events if they are in the right place in a catchment (Acreman and Holden, 2013; Ameli and Creed, 2019; Wu
18 et al., 2020). There is less data on the potential for natural flood management in tropical compared to
19 temperate catchments, however (Ogden et al., 2013) showed that flooding was reduced from a secondary
20 forested catchment compared to those which were pasture or a mosaic of forest, pasture and subsistence
21 agriculture. EbA approaches to reduce flooding can be applied within urban areas, as well as in rural
22 catchments, as in Durban (Section 2.6.5.7), although its effectiveness will depend on its being implemented
23 at a sufficient scale and in the right locations (Hobbie and Grimm, 2020; Costa et al., 2021). which may in
24 turn provide protection to downstream urban communities.
25
26 Protecting and restoring natural river systems, natural vegetation cover within catchments and integrating
27 agro-ecological techniques into agricultural systems can also help to maintain and manage water supplies for
28 human use, under climate change, including during drought periods, by storing water in catchments and
29 improving water quality (Taffarello et al., 2018; Agol et al., 2021; Khaniya et al., 2021) (Lara et al., 2021)
30 showed that replacing a non-native Eucalyptus plantation in Chile with native forest caused base flow
31 increased by 28% to 87% during the restoration period compared to pre-treatment, and found it remained
32 during periods with low summer precipitation
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1
2 EbA can operate at a range of different scales, from local to catchment to region. At the local scale, there is
3 a variety of circumstances in which microclimates can be managed and local temperatures lowered by the
4 presence of vegetation (Table 2.7), and these EbA techniques are now being used more widely. In both urban
5 and agricultural situations, shade trees are a traditional technique, which can be applied to contemporary
6 climate change adaptation. Shading of water courses can lower temperatures, as reported in Section 2.6.2,
7 above, which can allow species to survive locally, as well as supporting diversity it can help to maintain are
8 important fisheries, including of Salmonid fish (O'Briain et al., 2020). Within cities, green spaces, including
9 parks, local nature reserves and green roofs and walls can also provide cooling as a result of evapo-
10 transpiration (Bowler et al., 2010a; Aram et al., 2019; Hobbie and Grimm, 2020), although this may be
11 reduced in drought conditions.
12
13 Wildfire is an increasing risk for people as well as to ecosystems, in many parts of the world. As discussed
14 in Section 2.4.4.2, this is the result not just of climate change but also past management practices, including
15 fire suppression. Better fire management including reinstating more natural fire regimes can reduce risks.
16
17 EbA is usually a place-specific approach and a number of studies have documented how attempts to
18 implement it without an understanding of local circumstances and full engagement of local communities
19 have been unsuccessful (Nalau et al., 2018). Since AR5, a number of studies have considered the factors that
20 are important for environment adaptation programmes and projects (UNFCCC, 2015; Nalau et al., 2018;
21 Duncan et al., 2020; Network and ENCA, 2020; Townsend et al., 2020). Considering these sources, others
22 described above and the case studies presented, in 2.6.5, a number of requirements for effective
23 implementation of EbA can be identified, including the following:
24
25 • Targeting of the right EbA measure in the right location
26 • Decision-making at the appropriate level of governance with participation from all affected communities
27 • Integration of Local Knowledge and Indigenous Knowledge & capacity into decision-making and
28 management of project
29 • Involvement of government and non-government stakeholders
30 • Full integration of EbA with other policy areas, including agriculture, water resources and natural
31 resource protection
32 • Protection and if possible improvement of incomes of local people.
33 • Effective institutional support to manage finances and implementation of projects and programmes.
34 • Time -many EbA interventions take time to establish e.g. trees to grow, wetlands recover
35 • Monitoring of intended outcomes and other impacts and communication of results
36
37 Whilst it is essential to develop place-specific EbA measures, with full engagement of local communities, it
38 is worth noting that new opportunities may emerge that would not have been possible in the past. As the
39 climate changes, novel ecosystems may emerge with no present day analogue which have the potential to
40 provide different adaptation benefits and societies may be more willing to adopt transformative approaches
41 (Colloff et al., 2017; Lavorel et al., 2020).
42
43 Increasingly it is essential to integrate adaptation and the protection of biodiversity with land based climate
44 change mitigation initiatives; this is discussed in more detail in Cross-Chapter Box NATURAL, this
45 Chapter. The new IUCN standard (IUCN, 2020, Global standard) offers a basis for assessing whether actions
46 are true Nature-based Solutions and take account of the wider factors necessary for success.
47
48 Whilst policy interest is growing and there is an increasing deployment of EbA there is still a long way to go
49 in delivering it full potential (Huq and Stubbings, 2015) and significant institutional and cultural barriers
50 remain(Huq et al., 2017; Nalau et al., 2018). Nevertheless it is increasingly clear that EbA can offer a
51 portfolio of effective measures to reduce risks from climate change to people at the same time as benefiting
52 biodiversity (robust evidence, high agreement), providing they are deployed with careful planning in a way
53 that is appropriate local ecological and societal contexts (robust evidence, high agreement).
54
55 2.6.4 Adaptation for Increased Risk of Disease
56
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1 Low-probability events can be very high impact (for example, the transmission of the SARS-CoV-2 from
2 wild animals to humans). A robust disease risk reduction policy would include utilizing a One Biosecurity
3 approach (Hulme, 2020) with actions to reduce disease risk across multiple sectors and from a variety of
4 anthropogenic drivers, including climate change, even if there is high uncertainty in projected risk (see
5 Cross-Chapter Boxes: ILLNESS, COVID, DEEP). Kraemer et al. (2019) found that vector importation was a
6 key risk factor and focus should be on preventing invasive species introductions. Further, many neglected
7 tropical diseases (NTDs) are also VBDs, and the UN SDG of good health and well-being explicitly calls for
8 increased control and intervention with a focus on emergency preparedness and response (Stensgaard et al.,
9 2019a). Online tools are being developed to warn conservation biologists when species of conservation
10 concern are at greater risk of disease outbreaks due to environmental changes (e.g., for Hawaiian
11 Honeycreepers and avian malaria (Berio Fortini et al., 2020) and for coral diseases (Caldwell et al., 2016)).
12 Forecasting models to warn of human disease outbreaks like malaria and dengue are also now available, with
13 findings that multiple model ensemble forecasts outperform individual models (Lowe et al., 2013; Lowe et
14 al., 2014; Lowe et al., 2018; Zhai et al., 2018; Johansson et al., 2019; Tompkins et al., 2019; Muñoz et al.,
15 2020; Colón-González et al., 2021; Petrova et al., 2021). Improving vector-borne disease and NTD public
16 health responses will require multi-disciplinary teams capable of interpreting, analyzing, and synthesizing
17 diverse components of complex ecosystem-based studies for effective intervention (Mills James et al., 2010;
18 Rubin et al., 2014; Valenzuela and Aksoy, 2018), broad epidemiological and entomological surveillance
19 (Depaquit et al., 2010; Lindgren et al., 2012; Springer et al., 2016), and community-based disease control
20 programs that build local capacity (Andersson et al., 2015; Jones et al., 2020b).
21
22
23 [START CROSS-CHAPTER BOX ILLNESS HERE]
24
25 Cross-Chapter Box ILLNESS: Infectious Diseases, Biodiversity and Climate: Serious Risks Posed by
26 Vector- and Water-borne Diseases
27
28 Authors: Marie-Fanny Racault (United Kingdom/France, Chapter 3), Stavana E. Strutz (USA, Chapter 2),
29 Camille Parmesan (France/United Kingdom /USA, Chapter 2), Rita Adrian (Germany, Chapter 2), Guéladio
30 Cissé (Mauritania/Switzerland/France, Chapter 7), Sarah Cooley (USA, Chapter 3), Adugna Gemeda
31 (Ethiopia, Chapter 9), Nathalie Jeanne Marie Hilmi (Monaco/France, Chapter 18), Salvador E. Lluch-Cota
32 (Mexico, Chapter 5), Gretta Pecl (Australia, Chapter 11), David Schoeman (Australia, Chapter 3), Jan C.
33 Semenza (Italy, Chapter 7), Maria Cristina Tirado (USA/Spain, Chapter 7), Gautam Hirak Talukdar (India,
34 Chapter 2), Yongyut Trisurat (Thailand, Chapter 2), Meghnath Dhimal (Nepal, CA), Luis E. Escobar
35 (Guatemala/Columbia/USA, CA), Erin Mordecai (USA, CA), A. Townsend Peterson (USA, CA), Joacim
36 Rocklöv (Sweden, CA), Marina Romanello (United Kingdom, CA)
37
38 Climate change is altering the life cycles of many pathogenic organisms and changing the risk of vector- and
39 water-borne infectious diseases transmission to humans (high confidence). Re-arrangement and emergence
40 of some diseases are already observed in temperate-zone and high-elevation areas, and coastal areas
41 (medium to high confidence). Shifts in the geographic and seasonal range suitability of pathogens and
42 vectors are related to climatic-impact drivers (warming, extreme events, precipitation, humidity) (high to
43 very high confidence), but there are substantial non-climatic drivers (land use change, wildlife exploitation,
44 habitat degradation, public health and socio-economic conditions) that affects the attribution of the overall
45 impacts on prevalence or severity of some vector- and water-borne infectious diseases over recent decades
46 (high confidence). Adaptation options that involve sustained and rapid surveillance systems, and the
47 preservation and restoration of natural habitats, with their associated higher levels of biodiversity, both
48 marine and terrestrial, will be key to reducing risk of epidemics and large-scale disease transmissions
49 (medium confidence).
50
51 Since AR5, further evidence is showing that climate-related changes in the geographic and seasonal range
52 suitability of pathogens and vectors and in the prevalence or new emergence of vector- and water-borne
53 infectious diseases have continued across many regions worldwide and are sustained over decadal time
54 scales (medium to high confidence) (WGII Sections 2.4.2.5, 3.5.5.3, 7.2, 7.3, 9.10.1.2.1; Harvell et al., 2009;
55 Garrett et al., 2013; Burge et al., 2014; Guzman and Harris, 2015; Baker-Austin et al., 2018; Watts et al.,
56 2019; Semenza, 2020; Watts et al., 2021) . Ecosystem-mediated infectious diseases at risk of increase from
57 climate change include water-borne diseases associated with pathogenic Vibrio species (e.g., those causing
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1 cholera and vibriosis), and harmful algal blooms (e.g., ciguatera-fish poisoning) (SROCC 5.4.2.1.1, Box 5.4,
2 AR6 WGII 3.5, Table 3.S.3, 5.12; Baker-Austin et al., 2013; Levy, 2015; Trtanj et al., 2016; Ebi Kristie et
3 al., 2017; Mantzouki et al., 2018; Nichols et al., 2018), and vector-borne diseases associated with arthropods
4 (e.g., malaria, dengue, chikungunya, Zika virus, West Nile virus, and Lyme disease), helminths (e.g.,
5 schistosomiasis) and zoonotic diseases associated with cattle and wildlife (e.g., leptospirosis) (medium to
6 high confidence) (Sections 2.4.2.7, 3.5, 7.2, 7.3, 9.10.1.1.1, 13.7.1.2, 14.4.6, Cross-Chapter Box COVID in
7 Chapter 7; Table Cross-Chapter Box ILLNESS.1; SR1.5 3.4.7.1; Ebi et al., 2021) .
8
9 The attribution of observed changes in disease incidence partly or fully to climatic-impact drivers remains
10 challenging because of the difficulty of accurately capturing the contributions of multiple, interacting, and
11 often nonlinear underlying responses of host, pathogen, and vector, which can be influenced further by non-
12 climate stressors and the long history of anthropogenic disturbance. Disease emergence in new areas requires
13 independent drivers to coincide (i.e., increasing climate suitability for pathogen or vector survival and
14 competence/capacity, and introduction of the pathogen via mobility of human populations). Further, the
15 extent to which changes in ecosystem-mediated diseases impact human health is highly dependent upon
16 local socio-economic status, sanitation, medical systems, and practices (Section 2.4.2.5; Figure FAQ2.3.1;
17 Gething et al., 2010; Lindgren et al., 2012; Mordecai et al., 2013; Liu-Helmersson et al., 2014; Bhatt et al.,
18 2015; Morin et al., 2015; Ryan et al., 2015; Wesolowski et al., 2015; Stanaway et al., 2016; Yamana et al.,
19 2016; Mordecai et al., 2017; Tesla et al., 2018; Ryan et al., 2019; Shah et al., 2019; Iwamura et al., 2020;
20 Mordecai et al., 2020; Colón-González et al., 2021; Ryan et al., 2021).Thus, the links between climate
21 change, ecosystem change, health and adaptation need to be considered concurrently (AR6 WGII 2.4, 3.5.3,
22 7.2, 7.3, 4.3.3, 6.2.2.3, Table 2.S.1).
23
24
25 Table Cross-Chapter Box ILLNESS.1: Observed climate change impacts on cholera, dengue, and malaria. 1)
26 Cholera: Endemicity based on Ali et al., 2015. Changes (2003-2018) in suitability for coastal Vibrio cholerae estimated
27 from model observations driven by sea-surface temperature (SST) and chlorophyll-a (CHL) concentration (Escobar et
28 al., 2015; Watts et al., 2019). Vulnerabilities based on Sigudu et al., 2015, Agtini et al., 2005, and Sack et al., 2003. 2)
29 Dengue: Endemicity based on Guzmen et al., 2015. 3) Malaria: Endemicity based on Phillips et al. 2017, and WHO
30 Global Malaria Programme. Impacts of climate change on diseases and their vectors are most evident at the margins of
31 current distributions. However, climate change is difficult to implicate in areas with extensive existing transmission and
32 vector/pathogen abundance, and in particular is difficult to separate from concurrent directional trends in disease
33 control, changes in land use, water access, socioeconomic and public health conditions. As a result, while many studies
34 indicate increasing climate suitability of some areas for cholera, dengue, and malaria, the degree to which these changes
35 can be attributed to climate change remains challenging. For these cases, confidence statements of low, medium, or high
36 reflect confidence that variation in the disease and/or vector/pathogen is associated with variation in climate drivers,
37 rather than with directional climate change per se. Acronyms: ONI (Oceanic Niño Index), Tmin (minimum
38 temperature), SPI (Standardised Precipitation Index), LST Land-surface temperature. Full references for this table can
39 be seen in supplemental table 6 (see Table 2.S.6).
Cholera Dengue Malaria
Africa
Endemicity Endemic Endemic in sub-Saharan Africa Endemic
but not S South Africa
Climate drivers Disease incidence: NE Africa, W Africa: Temp ( medium to
Central Africa & Madagascar: high confidence)
Rainfall ( medium confidence) E Africa: Temp medium to high
SE Africa: Rainfall, LST, SST, confidence)
Plankton (medium to high
confidence )
ES Africa: SST, CHL (low
confidence due to limited
evidence)
W Africa: Rainfall (floods), LST,
SST (medium to high confidence)
Change and Area of coastline suitable for Potentially expanding ( low E Africa: Upward shift and
Confidence outbreak: N&W Africa: Increase confidence) increase in malaria & Anopheles
(low to medium confidence) Dengue and Ae. aegypti present spp. in highland areas (medium to
C & E Africa: No change (low to but underdetected in climatically high confidence )
medium confidence ) suitable areas. Widespread decreases due to
S Africa: Decrease (low to malaria control ( medium
medium confidence) confidence) and warming climate
(low confidence)
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Vulnerabilities ES Africa: women of all ages
more affected than men by
outbreaks
Asia
Endemicity Endemic Endemic in S Asia, SE Asia, and Endemic in S Asia, SE Asia,
E Asia Partially endemic in E Asia
Climate drivers Disease incidence: E Asia: SST, S Asia: Rainfall, Temp, S Asia: Rainfall, Temp (medium
CHL, Sea Level (medium to high Humidity (medium confidence) to high confidence)
confidence) SE Asia: Rainfall, Temp medium SE Asia: Rainfall, Temp
S Asia: SST, CHL, LST, confidence) (medium confidence)
Rainfall(floods) (high to very E Asia: Rainfall, Temp,
high conficende) Typhoons (low confidence)
Change and Area of coastline suitable for SE Asia: Increase (low S Asia: Increase (medium
Confidence outbreak: Increase (low to confidence) confidence)
medium confidence) S Asia: Increase (medium
confidence)
E Asia: Increase (low confidence)
Vulnerabilities SE Asia: infants (<9 years) with
highest incidences of cholera
S Asia: older children and young
adults (16-20 years old) more
frequently reported with cholera
than non-cholera diarrhoea
Australasia
Endemicity Not endemic Partially endemic in N Australia Not endemic
Climate drivers No evidence for disease Rainfall, Temp (low confidence)
incidence
Change and Area of coastline suitable for Increase in sporadic outbreaks No change
Confidence outbreak: No change (low to due to climate change (low
medium confidence) confidence)
Central America
Endemicity Not endemic Endemic Partially endemic
Climate drivers No evidence for disease ONI, SST, Tmin, Temp,
incidence Rainfall, Drought (low
confidence)
Change and Areas of coastline suitable for Increasing due to climate (low Overall decrease not linked to
Confidence outbreak: Decrease (low to confidence) climate change. Focal increases
medium confidence) Upward expansion of Ae. aegypti due to human activities.
(low confidence)
South America
Endemicity Epidemic Endemic in all regions except S Endemic
South America
Climate drivers Abundance of coastal V. Temp, Prec, Drought N South America: Temp (low
cholerae: NW South America: confidence)
SST, Plankton (low to medium N SE South America: Tmax,
confidence) Tmin, humidity (low confidence)
Change and Area of coastline suitable for Increasing due to urbanization Higher elevation regions:
Confidence outbreak: No change (low to and decreased vector control Increase (low confidence)
medium confidence) programmes, not strongly linked
to climate
Europe
Endemicity Not endemic S Europe: focal outbreaks Not endemic
Climate drivers No evidence for disease
incidence
Abundance of coastal V.
cholerae: N Europe: SST,
Plankton (medium confidence)
Change and Area of coastline suitable for Mediterranean No change
Confidence outbreak: Increase (low to regions of S Europe: Outbreaks
medium confidence) (low confidence)
North America
Endemicity Not endemic Partially endemic in S North Not endemic
America
Climate drivers Area of coastline suitable for Winter minT (Low)
outbreak: Increase (low to
medium confidence)
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Change and No evidence for disease Declining No change
Confidence incidence
Abundance of coastal V.
cholerae: EN America: SST
(Low due to limited evidence)
Small Islands
Endemicity Epidemic Endemic in many small islands in Endemic in many small islands in
the Tropics the Tropics
Climate drivers Disease incidence: Caribbean: Caribbean: SPI, Tmin (low
SST, LST, Rainfall (low to confidence)
medium confidence)
Change and Area of coastline suitable for Increasing (low confidence) Decrease in Caribbean not linked
Confidence outbreak: Caribbean & Pacific to climate
Small Island: Decrease (low to
medium confidence)
1
2
3 Observed and projected changes
4
5 In aquatic systems, at least 30 human pathogens with water infection-routes (freshwater and marine) are
6 affected by climate change (Section 3.5.3 Table SM3.G; Nichols et al., 2018) . Warming, acidification,
7 hypoxia, sea-level rise, and increases in extreme weather and climate events (e.g. marine heatwaves, storm
8 surges, flooding, and drought), which are projected to intensify in the 21st century (high confidence) (AR6
9 WGI SPM B2.2, B.3.2), are driving species’ geographic range shifts and global rearrangements in the
10 location and extent of areas with suitable conditions for many harmful pathogens, including viruses, bacteria,
11 algae, protozoa, and, helminths (high confidence) (Sections 2.3, 2.4.2.7, 3.5.5.3, SROCC 5.4.2.1.1, Box 5.4;
12 Trtanj et al., 2016; Ebi Kristie et al., 2017; Manning and Nobles, 2017; Pecl et al., 2017; Mantzouki et al.,
13 2018; Nichols et al., 2018; Bindoff et al., 2019; Kubickova et al., 2019; Watts et al., 2019; Watts et al., 2020;
14 Watts et al., 2021).
15
16 Incidence of cholera and Vibrio-related disease outbreaks has been shown to originate primarily in coastal
17 regions, and then spread inland via human transportation. Our understanding of impacts of climate drivers on
18 the dynamics of Vibrio-related infections have been strengthened through improved observations from long-
19 term monitoring programs (e.g., (Vezzulli et al., 2016)), and statistical modelling supported by large-scale
20 and high-resolution satellite observations of climate drivers (high confidence) (e.g., Baker-Austin et al.,
21 2013; Escobar et al., 2015; Jutla et al., 2015; Martinez et al., 2017; Semenza et al., 2017; Racault et al.,
22 2019; Campbell et al., 2020).
23
24 The coastal area suitable for V. cholerae (the causative agent for cholera) has increased by 9.9% globally
25 compared to a 2000s baseline (Escobar et al., 2015; Watts et al., 2019). The poleward expansion of the
26 distribution of Vibrio spp. has increased the risk of vibriosis outbreaks in northern latitudes. Specifically, the
27 coastal area suitable for Vibrio infections in the past 5 years has increased by 50.6% compared with a 1980s
28 baseline at latitudes of 40–70°N; in the Baltic region, the highest-risk season has been extended by 6.5
29 weeks over the same periods (Watts et al., 2021). Already, studies have noted greater numbers of Vibrio-
30 related human infections, and most notably disease outbreaks linked to extreme weather events such as heat
31 waves in temperate regions such as Northern Europe (Baker-Austin et al., 2013; Baker-Austin et al., 2017;
32 Baker-Austin et al., 2018) (high confidence). By the end of the 21st century, under RCP6.0, the number of
33 months of risk of Vibrio illness is projected to increase in Chesapeake Bay by 10.4±2.4%, with largest
34 increases during May and September, which are the months of strong recreational and occupational use,
35 compared to a 1985-2000 baseline (Jacobs et al., 2015; Davis et al., 2019a). In the Gulf of Alaska, the
36 coastal area suitable for Vibrio spp. is projected to increase on average by 58%±17.2% in summer under
37 RCP6.0 by the 2090s, compared to a 1971-2000 baseline (low to medium confidence) (Jacobs et al., 2015).
38
39 On land, increased global connectivity and mobility, unsustainable exploitation of wild areas and species,
40 land conversion (agricultural expansion, intensification of farming, deforestation, infrastructure
41 development), together with climate-change-driven range shifts of species and human migration (Cross-
42 Chapter Box MOVING PLATE in Chapter 5), have modified interfaces between people and natural systems
43 (IPBES, 2018a). Climate-driven increase in temperature, frequency and intensity of extreme events, and
44 changes in precipitation and relative humidity, have provided opportunities for re-arrangements of disease
45 geography and seasonality, and emergence into new areas (high confidence) (Section 2.4.2.7). In particular,
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1 malaria has expanded into higher elevations in recent decades and although climate change attribute remains
2 challenging (Hay et al., 2002; Pascual et al., 2006; Alonso et al., 2011; Campbell et al., 2019c), evidence that
3 the elevational distribution of malaria has tracked warmer temperatures is compelling for some regions (Siraj
4 et al., 2014). Models based on both empirical relationships between temperature and the Anopheles mosquito
5 and Plasmodium parasite traits that drive transmission (Mordecai et al., 2013; Yamana Teresa and Eltahir
6 Elfatih, 2013; Johnson et al., 2015) and existing mosquito distributions (Peterson, 2009) predict that
7 warming will increase the risk of malaria in highland East Africa and Southern Africa, while decreasing the
8 risk in some lowland areas of Africa, as temperatures exceed the thermal optimum and upper thermal limit
9 for transmission (Peterson, 2009; Yamana Teresa and Eltahir Elfatih, 2013; Ryan et al., 2015; Watts et al.,
10 2021).
11
12 In contrast to malaria, dengue has expanded globally since 1990, particularly in Latin America and the
13 Caribbean, South Asia, and sub-Saharan Africa (Stanaway et al., 2016). While urbanization, changes in
14 vector control, and human mobility play roles in this expansion (Gubler, 2002; Åström et al., 2012;
15 Wesolowski et al., 2015), the physiological suitability of temperatures for dengue transmission is also
16 expected to have increased as climates have warmed (Colón-González et al., 2013; Liu-Helmersson et al.,
17 2014; Mordecai et al., 2017; Rocklöv and Tozan, 2019). Models predict that dengue transmission risk will
18 expand across many tropical, subtropical, and seasonal temperate environments with future warming
19 (Åström et al., 2012; Colón-González et al., 2013; Ryan et al., 2019; Iwamura et al., 2020; Watts et al.,
20 2021)).
21
22 Adaptation options
23
24 During the 21st century, public health adaptation measures (Figure Cross-Chapter Box ILLNESS.2) have
25 been put in place in attempts to control or eradicate a variety of infectious diseases by improving
26 surveillance and early detection systems; constraining pathogen, vector, and/or reservoir host distributions
27 and abundances; reducing likelihood of transmission to humans; and improving treatment and vaccination
28 programs and strategies (medium to robust evidence, medium to high agreement) (Chinain et al., 2014;
29 Adrian et al., 2016; Friedman et al., 2017; Konrad et al., 2017; Semenza et al., 2017; Borbor-Córdova et al.,
30 2018; Rocklöv and Dubrow, 2020). In addition, effective management and treatment of domestic and
31 wastewater effluent, through better infrastructure and preservation of aquatic systems acting as natural water
32 purifiers, have been key to securing the integrity of the surrounding water bodies, such as groundwater,
33 reservoirs and lakes, and agricultural watersheds, as well as protecting public health (high confidence)
34 (Okeyo et al., 2018; Guerrero-Latorre et al., 2020; Kitajima et al., 2020; Sunkari et al., 2021). The
35 preservation and restoration of natural ecosystems, with their associated higher levels of biodiversity, have
36 been reported as significant buffers against epidemics and large-scale pathogen transmission (medium
37 confidence) (Johnson and Thieltges, 2010; Ostfeld and Keesing, 2017; Keesing and Ostfeld, 2021). Further,
38 the timely allocation of financial resources and sufficient political will in support of a “One Health”
39 scientific research approach, recognising the health of humans, animals and ecosystems as interconnected
40 (Rubin et al., 2014; Whitmee et al., 2015; Zinsstag et al., 2018), holds potential for improving surveillance
41 and prevention strategies that may help to reduce the risks of further spread, and new emergence of
42 pathogens and vectors (medium confidence) (Destoumieux-Garzón et al., 2018; Hockings et al., 2020;
43 Volpato et al., 2020; Hopkins et al., 2021; Services and Ecosystem, 2021).
44
45
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1
2 Figure Cross-Capter Box ILLNESS.1: Adaptation measures to reduce risks of climate change impact on water- and
3 vector-borne diseases. Impacts are identified at three levels: 1) impact on pathogen, host/vector distributions and
4 abundance; 2) impact on pathogen-host transmission cycle occurrence and efficiency; and 3) impact on likelihood of
5 transmission to humans. Adaptation typology is based on (Biagini et al., 2014; Pecl et al., 2019). For each type of
6 adaptation, examples are provided with their level of evidence and agreement.
7
8 [END CROSS-CHAPTER BOX ILLNESS HERE]
9
10
11 2.6.5 Adaptation in Practice: Case Studies and Lessons Learned
12
13 Adaptation plans for biodiversity and EbA have been adopted in many places and different scales but it is
14 difficult to get a systematic overview of adaptation in practice. We have therefore reviewed a series of
15 contrasting case studies to illustrate the some key issues. There is a pressing need for more thorough
16 monitoring and evaluation of adaptation to assess effectiveness. Climate change adaptation is conceptually
17 difficult to measure but it is possible to test which techniques work in reducing vulnerability and monitor
18 their deployment (Morecroft et al., 2019).
19
20 Adaptation can take place at a range of scales with specific projects nested within overarching national
21 strategies. Small scale projects can be adaptation focused, but in larger scale adaptation is often integrated
22 with wider objectives. Within an urban or peri-urban context, the benefits of natural and semi-natural areas
23 for health and well- help to justify support for EbA. Economic wellbeing is also an important factor in many
24 cases, whether as, in Durban (Section 2.6.5.7), by providing new job opportunities or, in the Andes (Section
25 2.6.5.4) by supporting long-established agricultural practice. Action on the ground often depends on factors
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1 at a range of scales, for example, a local plan, a national strategy and international funding. Within Durban,
2 partnership between local communities, local authorities and the academic community were essential,
3 together with an international context. Nevertheless, there are examples of communities using traditional or
4 local knowledge to adapt to changing circumstances, with little or no external input, (Section 2.6.5.4). They
5 are, however, limited in their scope to adapt by factors beyond their direct control.
6
7 Specific interventions to protect species from climate change, such as the case of South African
8 penguins(Section 2.6.5.5) and the Tasmanian Wilderness World Heritage Area (Section 2.6.5.8), are rare.
9 However, in countries where nature reserves are actively managed or where ecosystem restoration projects
10 are progressing, local practitioners may use local knowledge to adapt to weather conditions and their
11 associated effects (fire or water shortage for example). This is good practice, but it may not be sufficiently
12 to address likely future changes in climate (Duffield et al., 2021). Training and resources to support
13 conservation practitioners are becoming available to help address this. Examples include the Climate Change
14 Adaptation Manual in England (Section 2.6.5.2), and The Alliance for Freshwater Life
15 (https://allianceforfreshwaterlife.org), that provide expertise for the sustainable management of freshwater
16 biodiversity (Darwall et al., 2018).
17
18 Adaptation is widely recognised as important for national conservation policy and is being considered in a
19 variety of countries (Section 2.6.5.2, 2.6.5.3). Adaptation in this strategic context includes decisions about
20 the selection and objectives for protected areas, for example identifying places which can act as refugia. It
21 can also mean recognising where protected areas remains important but will support a changing range of
22 species and ecosystems. This is important for directing resources effectively and ensuring that site
23 management remains appropriate. There are however often major uncertainties and the extent to which there
24 will be a need for more radical measures will depend on success in reducing greenhouse gas emissions
25 globally. A global rise of 1.5–2°C would require relatively incremental adjustments to conservation
26 management in many parts of the world, but a 3‒4°C rise would require radical, transformational changes to
27 maintain many species and ecosystem services (Morecroft et al., 2012).
28
29 Whilst adaptation strategies for conservation are relatively common, at least at an outline level,
30 implementation is slow in most places. This may partly reflect lack of resources for conservation in many
31 parts of the world; however, another barrier is that people often value protected sites in their present form.
32 Actions, which might jeopardise this, are inevitably a last resort. Initiatives to engage wider communities in
33 discussions are likely to be essential in gaining support for such changing approaches.
34
35 EbA and adaptation for biodiversity are intrinsically linked and the largest scale interventions for adaptation
36 in ecosystem have tended to bring together both elements. For example adaptation to reduce flood risk by
37 habitat creation and using natural processes, (Section 2.6.5.2, Cross-Chapter Box SLR in Chapter 3), such as
38 by re-naturalising straightened river systems or creating wetlands for water storage, offers the potential to
39 meet multiple objectivesand has increased overall funding available for ecosystem restoration.
40
41 2.6.5.1 Case study: Assisted Colonisation / Managed Relocation in Practice
42
43 Scale: Global
44 Issue: Helping species move to track shifting climate space
45
46 Managed relocation (assisted migration, assisted colonisation) is the movement of species, populations, or
47 genotypes to places outside the areas of their historical distributions (Hoegh-Guldberg et al., 2008) and may
48 be an option where they are not able to disperse and colonise naturally. It requires careful consideration of
49 scientific, ethical, economic and legal issues between the object of relocation and the receiving ecosystem
50 (Hoegh-Guldberg et al., 2008; Richardson et al., 2009; Schwartz et al., 2012).
51
52 Individual cases show that assisted migration can be successful. Anich & Ward (2017) extended the
53 geographic breeding range of a rare bird, Kirtland's warbler, Setophaga kirtlandii, by 225km by using song
54 playbacks to attract migrating individuals. Wadgymar (2015) successfully transplanted an annual legume,
55 Chamaecrista fasciculata, to sites beyond its current poleward range limit, while Liu (2012) found that all
56 but one of 20 orchid species survived when transplanted to higher elevations than their current range limits.
57 After introducing two British butterfly species to sites ∼65 and ∼35 km beyond their poleward range
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1 margins, Willis (2009) observed that both introduced populations grew, expanded their ranges and survived
2 for at least 8 years.
3
4 Butterflies have been favoured subjects for assisted migration in response to regional climate warming, since
5 they are easy to move and their range dynamics have been extensively studied. The Chequered Skipper
6 butterfly, Carterocephalus palaemon, became locally extinct in England in the 1970's, in an area not close to
7 either the species' poleward or equatorial range limits. Nonetheless, Maes (2019) consider climate a crucial
8 parameter for re-introduction, using SDMs both for choosing the source population in Belgium and
9 introduction site.
10
11 Success of assisted migration for conservation purposes has been variable. Bellis (2019), identified 56
12 successes and 33 failures among 107 translocations of insects that had been undertaken explicitly for
13 conservation purposes. They concluded failure was most strongly associated with low numbers of
14 individuals released. Another potential source of failure is local adaptation: there is good evidence that
15 adaptive differences among potential source populations can be important. For example, the transplants of C.
16 fasciculata were more successful when sourced from the most poleward existing sites, while individuals
17 from more equatorial habitats performed poorly even when artificially warmed (Wadgymar et al., 2015).
18
19 2.6.5.2 Case study: Adaptation for conservation and Natural Flood Management in England, United
20 Kingdom
21
22 Scale: National
23 Issue: National approach to adaptation in the natural environment
24
25 Climate change threats to biodiversity in England include range retraction of cold adapted species and effects
26 of more frequent extreme weather events such as droughts. These threats are exacerbated by land use and
27 management: with habitats fragmented, land often drained and rivers straightened. There are also risks to
28 people, which are exacerbated by environmental factors, including flooding and over-heating in urban areas.
29 A National Adaptation Programme, provides a broad policy framework for England and includes a chapter
30 on the natural environment. There are also adaptation plans produced by public bodies such as Natural
31 England, the conservation agency and the Environment Agency, with a wide range of responsibilities
32 including flood defence. The principles of climate change adaptation are well established in the UK
33 conservation community and resources are available. Natural England has published a Climate Change
34 Adaptation Manual jointly with the Royal Society for the Protection of Birds–a major conservation NGO
35 (England and RSPB, 2020) and spatial mapping tool for climate change vulnerability (Taylor et al., 2014).
36
37 (Duffield et al., 2021) found that awareness of the need for adaptation was common amongst nature reserve
38 managers and that they were implementing actions that might building resilience to climate change, such as
39 restoring ecosystem processes and reducing fragmentation. . There is a recognition that it will be necessary
40 to change management objectives of protected sites to adjust to changing circumstances but there was little
41 implementation of such changes (Duffield et al., 2021). The main examples of managing change, was at the
42 coast where rising sea level is causing transitions from terrestrial and freshwater systems to coastal and
43 marine ones.
44
45 A range of EbA approaches are starting to contribute to adaptation in England but the best developed is
46 Natural Flood Management (NFM): restoring natural processes and natural habitats to reduce flood risk
47 (Wingfield et al., 2019). Over the last decade, a series of NFM projects have been established in local areas.
48 The Environment Agency collated the evidence base on NFM (Burgess-Gamble et al., 2017) and was able to
49 draw on 65 case studies (Ngai et al., 2017), covering river and floodplain management, woodland
50 management, run-off management and coast and estuary management.
51
52 NFM includes a broad range of techniques, some of which, deliver real benefits for biodiversity and allow
53 natural ecological process to re-establish. Others, such as creating ‘woody debris dams’ – barriers artificially
54 constructed from tree trunks and branches in water courses to slow flow of water ‒will have fewer benefits,
55 although they may benefit some species. Dadson et al. (2017) concluded that ‘the hazard associated with
56 small floods in small catchments may be significantly reduced’ by natural flood management techniques.
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1 However, they noted that the most extreme flood events may overwhelm any risk management measures and
2 failed to find clear evidence of NFM in reducing flood risk downstream in large catchments.
3
4 There remain challenges in deploying NFM at larger scales, partly reflecting the time necessary to
5 demonstrate the effectiveness of pilot studies and build confidence and building stakeholder support is
6 important (Huq et al., 2017). There are now a number of examples of where collaborative initiatives between
7 local communities, land owners and government agencies have been successful in establishing effective
8 NFM schemes (Short et al., 2019).
9
10 2.6.5.3 Case Study: Protected Areas Planning in Response to Climate Change in Thailand
11
12 Scale: National
13 Issue: Protected area network planning
14
15 Many countries in the Association of South East Asian Nations (ASEAN) are expanding protected area
16 networks to meet the Aichi target 11 of at least 17% of terrestrial area protected and it is important to take
17 the effects of climate change into account. Existing protected areas in Thailand cover approximately 21% of
18 the land area, and it is one of the few tropical countries that passes the Aichi Target 11. Most protected areas
19 in Thailand were established on an ad hoc basis to protect remaining forest cover, and as a result do not
20 represent diverse habitats and their associated species (Chutipong et al., 2014; Tantipisanuh, 2016) and may
21 not be resilient to the interacting impacts of future land use and climate change (Klorvuttimontara et al.,
22 2011; Trisurat, 2018).
23
24 Recent research conducted in northern Thailand indicated that the existing protected areas (31% of the
25 region area) cannot secure viability of many medium- size and large mammals. Most species climate space
26 would substantially shift, bringing a risk of extinction. The model results based on the spatial distribution
27 model and network flow determined there was a need for expansion areas of 5,200 km2 or 3% of the region
28 to substantially minimise the high-risk level and increase the average coping capacity of the protection of
29 suitable habitats from 82% as the current plan to 90%. These results were adopted by the Thailand’s
30 Department of National Parks, Wildlife and Plant Conservation and included in the National Wildlife
31 Administration and Conservation Plan (2021-2031).
32
33 2.6.5.4 Case Study: Effects of Climate Change on Tropical High Andean Social Ecological Systems
34
35 Scale: Regional
36 Issue: Complex ramifications of glacial retreat on vegetation, animals, herders and urban populations
37
38 Accelerated warming is shrinking tropical glaciers at rates unseen since the middle of the Little Ice Age
39 (Rabatel et al., 2013; Zemp et al., 2015). Climate-driven upward migration of species associated with
40 warming and glacier retreat has modified species distribution and richness, and community composition
41 along the Andes altitudinal gradient (Seimon et al., 2017; Carilla et al., 2018; Zimmer et al., 2018; Moret et
42 al., 2019). Climate-driven glacier retreat alters hydrological regimes, impacting Andean pastoralists directly
43 (López-i-Gelats et al., 2016; Postigo, 2020; Thompson et al., 2021), and water provisioning to lowland
44 regions (Vuille et al., 2018; Hock et al., 2019; Orlove et al., 2019; Rasul and Molden, 2019). Drying
45 wetlands has modified alpine plant communities, which are relevant to storing carbon, regulating water, and
46 providing food for local livestock, leading to negative impacts on herders’ livelihoods (Dangles et al., 2017;
47 Polk et al., 2017; Postigo, 2020) and differently affecting the wild vicuña and the domesticated alpaca and
48 llama. Vicuña (Vicugna vicugna) and alpaca (Vicugna pacos) wool are important income sources for
49 indigenous communities and Llama (Lama glama) is the main source of meat. Vicuña is adjusting its feeding
50 behaviour and spatial distribution as vegetation migrates upwards (Reider and Schmidt, 2020), causing them
51 to roam outside protected areas and become vulnerable to illegal poaching.
52
53 Andean herders have responded to drying of grasslands by increasing livestock mobility, accessing new
54 grazing areas through kinship and leases, creating and expanding wetlands through building long irrigation
55 canals (of several km), limiting allocation of wetlands to new households, and sometimes cultivating grasses
56 (Postigo, 2013; López-i-Gelats et al., 2015; Postigo, 2020). These adaptive responses to regional climate
57 change are enabled by deeply-embedded indigenous institutions that have traditionally governed Andean
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1 pastoralists, but have become severely compromised by national socio-economic pressures (Valdivia et al.,
2 2010; Postigo, 2019; Postigo, 2020). For instance, water quality, access and control by local pastoralists has
3 declined due to new mining concessions in headwaters of Andean watersheds (Bebbington and Bury, 2009)
4 and diversion of water to lowland coastal desert for agricultural irrigation (Mark et al., 2017).
5
6 Glacier mass and runoff in the tropics are projected to reduce >70% and >10%, respectively, by 2100 under
7 RCP 2.6, RCP 4.5 and RCP 8.5 (Huss and Hock, 2018; Hock et al., 2019). In Peru, montane ice-field melt-
8 water provides 80% of the water resources for the arid coast where half the population lives (Thompson et
9 al., 2021). Increasing variability of precipitation has compromised rain-fed agriculture and power generation,
10 particularly in the dry season, exacerbating pressures for new water sources (Bradley et al., 2006; Bury et al.,
11 2013; Buytaert et al., 2017). Thus, there is risk of increasing conflicts between climate change adaptation to
12 benefit high Andean human and natural communities and adaptation to maintain water provisioning for
13 lowland agricultural and urban areas.
14
15 2.6.5.5 Case Study: Helping African Penguins Adapt to Climate Change
16
17 Scale: Regional / local
18 Issue: Adaptation for a threatened species
19
20 The African penguin, Spheniscus demersus, is the only resident penguin species on mainland Africa and
21 breeds in a handful of colonies in South Africa and Namibia. In 2017, penguins in Cape Town’s Boulders
22 Beach colony attracted almost one million visitors, providing 885 jobs and $18.9m revenue (Van Zyl and
23 Kinghorn, 2018).Ninety-six percent of the population has been lost since 1900, with a 77% decline in the last
24 two decades (Sherley et al., 2018) and by 2019 only 17,700 pairs remained (Sherley et al., 2020). The
25 species is listed as Endangered on the IUCN Red List (IUCN, 2018) and if this trajectory persists the African
26 penguin will become functionally extinct in the near future (Sherley et al., 2018).
27
28 Historically, hunting, egg and guano collection were the species’ main threats, but three aspects of climate
29 change now predominate. Firstly, a several-hundred-kilometre eastward shift in distributions of their main
30 prey species, anchovies and sardines, has reduced food availability (Roy et al., 2007; Crawford et al., 2011).
31 While adult penguins typically forage up to 400 km from their colonies, they are restricted to a ~20 km
32 radius from their colonies during breeding months (Ludynia et al., 2012; Pichegru et al., 2012). The resulting
33 food shortage at this critical time is compounded by competition with commercial fisheries and
34 environmental fluctuations (Crawford et al., 2011; Pichegru et al., 2012; Sherley et al., 2018). This has
35 impacted adults’ survival and their ability to raise high-quality offspring (Crawford et al., 2006; Crawford et
36 al., 2011; Sherley et al., 2013; Sherley et al., 2014).
37
38 Increasing heat wave frequency and intensity recorded in recent decades presents a second threat (van
39 Wilgen and Wannenburgh, 2016; Van Wilgen et al., 2016; Mbokodo et al., 2020). Nests were historically
40 built in insulated guano burrows, but are now frequently sited on open ground (Kemper et al., 2007;
41 Pichegru et al., 2012; Sherley et al., 2012). High temperatures frequently expose the birds to severe heat
42 stress, causing adults to abandon nests and resulting in mortality of eggs and chicks (Frost et al., 1976;
43 Shannon and Crawford, 1999; Pichegru et al., 2012).Intensifying storm surges and greater wave heights can
44 cause nest flooding (Randall et al., 1986; de Villiers, 2002).
45
46 The African penguin’s survival in the wild is dependent on the success of adaptation action. Increasing
47 access to food resources is a management priority (IUCN, 2018). One approach is to reduce fishing pressure
48 immediately around breeding colonies. An experiment excluding fishing around colonies since 2008 has
49 demonstrated positive effects (Pichegru et al., 2010; Pichegru et al., 2012; Sherley et al., 2015; Sherley et al.,
50 2018; Campbell et al., 2019b). A second approach is to establish breeding colonies closer to their prey. An
51 ongoing translocation initiative aims to entice birds eastwards to recolonise an extinct breeding colony and
52 potentially to establish a new one (Schwitzer et al., 2013; Sherley et al., 2014; International, 2018). Penguin
53 “look-alikes” or decoys, constructed from rubber and concrete, have been placed at the extinct colony site
54 and, along with call play-backs, give the illusion of an established penguin colony (Morris and Hagen,
55 2018). This approach has not yet proven successful.
56
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1 To promote on-site adaptation to heat extremes and flooding, initiatives are underway to provide cooler
2 nesting sites that also provide storm protection and are sufficiently above the high water level (Extinction,
3 2018; International, 2018). Artificial nest boxes of various designs and constructed from a range of materials
4 have been explored in combination with use of natural vegetation. Some designs have proven successful,
5 increasing breeding success (Kemper et al., 2007; Sherley et al., 2012), but the same designs have had less
6 success at other locations (Pichegru, 2013; Lei et al., 2014).
7
8 Hand-rearing and releasing African penguin chicks, including from eggs, has long proven valuable because
9 moulting parents, being shore-bound, are unable to feed late-hatching chicks. Since 2006, over 7,000-
10 orphaned chicks have been released into the wild as part of the Chick Bolstering Project with a success rate
11 of 77% (Schwitzer et al., 2013; Sherley et al., 2014; Klusener et al., 2018; SANCCOB, 2018). A new project
12 at Boulders Beach aims to use real-time weather station data, within-nest temperatures and known thresholds
13 of penguin heat stress as triggers for implementing a Heat Wave Response Plan. Drawing on well-
14 established chick-rearing facilities and a large body of expertise, this includes removing heat-stressed eggs
15 and birds, hand rearing and/or rehabilitation and release. It is hoped that such birds may be released at the
16 proposed new colony site.
17
18 2.6.5.6 Case study: Conserving Climate Change Refugia for the Joshua tree in Joshua Tree National Park,
19 California, United States of America
20
21 Scale: Local
22 Issue: Possible extirpation of a plant species from a national park
23
24 Joshua Tree National Park conserves 3200 km2 of Mojave and Sonoran Desert ecosystems. The climate of
25 the national park is arid, with a 1971-2000 average summer temperature of 27.3ºC ± 0.7ºC and average
26 annual precipitation of 170 ± 80 mm y-1 (Gonzalez et al., 2018). From 1895 to 2017, average annual
27 temperature increased at a significant (p < 0.0001) rate of 1.5 ± 0.1ºC century-1 and average annual
28 precipitation decreased at a significant (p = 0.0174) rate of -32 ± 12% century-1 (Gonzalez et al., 2018).
29 Anthropogenic climate change accounts for half the magnitude of a 2000–2020 drought in the Southwestern
30 USA, the most severe since the 1500s (Williams et al., 2020).
31
32 The national park was established to protect ecosystems and cultural features unique to the region,
33 particularly the Joshua tree (Yucca brevifolia), a tall, tree-like yucca that provides habitat for birds and other
34 small animals and holds cultural significance. The national park protects the southernmost populations of the
35 Joshua tree. Paleobiological data from packrat (Neotoma spp.) middens and fossil dung of the extinct Shasta
36 ground sloth (Nothrotheriops shastensis) show that Joshua trees grew 13 000‒22 000 thousand years before
37 present a across a wider range, extending as far as 300 km south into what is now México (Holmgren et al.,
38 2010; Cole et al., 2011). A major retraction of the range began ~11 700 thousand years before present,
39 coinciding with a warming in the region of 4ºC, caused by Milankovitch cycles, that marked the end of the
40 Pleistocene and beginning of the Holocene (Cole et al., 2011), suggesting a sensitivity of Joshua trees of 300
41 km latitude per 4ºC.
42
43 Under an emissions scenario that could increase park temperatures over 4ºC by 2100, suitable climate for the
44 Joshua tree could shift north and the species become extirpated from the park (Sweet et al., 2019). Plant
45 mortality would increase from drought stress and wildfires, which have been rare or absent in the Mojave,
46 but which invasive grasses have and may continue to fuel (Brooks and Matchett, 2006; DeFalco et al., 2010;
47 Abatzoglou and Kolden, 2011; Hegeman et al., 2014).
48
49 The national park had been trying to conserve the species wherever in the park it was found. The future risk
50 of extirpation prompted them to adapt conservation to focus on protecting potential refugia, where suitable
51 conditions may persist for the species into the future (Barrows et al., 2020). The national park used spatial
52 analyses of suitable climate to identify potential refugia under all emissions scenarios except for the highest
53 (Barrows and Murphy-Mariscal, 2012; Sweet et al., 2019). The park prioritises the refugia for removal of
54 invasive grasses and fire control (Barrows et al., 2020)and works to restore refugia that have burned in fires,
55 using native plants, including nursery-grown Joshua tree seedlings. The park and its partners are monitoring
56 plant species composition and abundance in the refugia for early warning of any changes (Barrows et al.,
57 2014).
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1
2 2.6.5.7 Case Study: Ecosystem based Adaptation in Durban, South Africa
3
4 Scale: Local
5 Issue: Ecosystem based adaptation (EbA) in a city and surrounding area
6
7 Durban was an early pioneer of EbA in a city context, establishing a Municipal Climate Protection
8 Programme (MCPP) in 2004 (Roberts et al., 2012). The City, situated in a global biodiversity hotspot
9 (Bank, 2016), has a rapidly growing population (approximately 3.5 million) and is highly fragmented
10 (Roberts et al., 2013). High levels of development, particularly in peri-urban areas, have encroached into
11 natural habitats (Bank, 2016). Degradation of the natural resource base in this way has direct economic and
12 financial costs, is threatening the City’s long-term sustainability, and is exacerbated by climate change
13 (Bank, 2016; Municipality, 2020). The impacts of climate change are anticipated to increase unless
14 appropriate mitigation and adaptation interventions are prioritised (Municipality, 2020). High rates of
15 poverty, unemployment and health problems have pushed Durban to explore a climate change adaptation
16 work stream within its MCPP (Roberts et al., 2013; Roberts et al., 2020b).
17
18 A single approach to adaptation is likely to be insufficient (Archer et al., 2014), and community-based
19 adaptation should be integrated as part of a package of tools applied at the city level. Durban’s climate
20 change adaptation work stream is composed of three separate components: municipal adaptation (adaptation
21 activities linked to the key functions of local government); community-based adaptation (focused on
22 improving the adaptive capacity of local communities); and a series of urban management interventions
23 (addressing specific challenges such as the urban heat island, increased storm water runoff, water
24 conservation and sea level rise) (Roberts et al., 2013).
25
26 Lessons learnt from Duban’s experience include the importance of meaningful partnerships, long-term
27 financial commitments (Douwes et al., 2015) and significant political and administrative will (Roberts et al.,
28 2012; Roberts et al., 2020b). Securing these requires strong leadership (Douwes et al., 2015), including from
29 local champions (Archer et al., 2014), even if EbA is considered cost-effective (Roberts et al., 2012). Natural
30 habitat restoration projects are seen as an ideal tool, as they combine mitigation outcomes with increased
31 adaptation capacity that not only reduces vulnerability of ecosystems and communities (Douwes et al.,
32 2016), but creates economic opportunities. These include direct job creation (Diederichs and Roberts, 2016;
33 Douwes and Buthelezi, 2016) with various spinoffs such as better education for schoolchildren (Douwes et
34 al., 2015). Indirect benefits, include better water quality and reduced flooding, are generated as a result of
35 improved ecosystem service delivery (Douwes and Buthelezi, 2016). In areas that are already developed,
36 opportunities for green roof infrastructure can yield reductions in roof storm water run-off (by approximately
37 60 ml/m2/minute during a rainfall event), slow release of water over time, and reduced temperatures on roof
38 surfaces (Roberts et al., 2012).
39
40 2.6.5.8 Case Study: Protecting Gondwanan refugia against fire in Tasmania, Australia
41
42 Scale: Local
43 Issue: Protection of rare endemic species
44
45 The Tasmanian Wilderness World Heritage Area (TWWHA) has a high concentration of ‘paleo-endemic’
46 plant species restricted to cool, wet climates and fire free environments, but recent wildfires have burnt
47 substantial stands, which are unlikely to recover (Harris et al., 2018b, Bowman et al., 2021, The 2016
48 Tasmanian). The fires led to government inquiries and a fire-fighting review, which have suggested changes
49 to management as that climate change will make such fires more likely in the future (Council, 2016; Press,
50 2016; Council, 2019).
51
52 The majority of the TWWHA is managed as a Wilderness Zone, where management is currently carried out
53 in a manner that allows natural processes to predominate. The exclusion of fire from stands of fire-sensitive
54 trees such as the Pencil pine, Athrotaxis cupressoides, is part of this management strategy, possible in the
55 past due to the moisture differential and lower flammability of these areas. However, in recent years, the
56 threat posed by extensive and repeated wildfires, and an increasing awareness that fire risk is likely to
57 increase (Fox-Hughes et al., 2014; Love et al., 2017; Love et al., 2019) have meant that more direct
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1 management intervention has been implemented. There has been a realisation that a “hands off” approach to
2 managing the threat will not be sufficient to protect the paleo-endemics. Not only is fire-fighting difficult in
3 the remote wilderness area, but limited resources mean that fire managers must prioritise where fires will be
4 fought when many fires are threatening towns and lives across the state simultaneously.
5
6 After wildfires in 2016 caused extensive damage (Bowman et al., 2020a), significant effort and resources
7 were spent trying to protect the remaining stands of Pencil pine during the 2019 fires, using new approaches
8 including the strategic application of long-term fire retardant and the installation of kilometres of sprinkler
9 lines (Council, 2019). These approaches are thought to have been effective at halting the fire and protecting
10 the high value vegetation in some situations. Impact reports are currently being finalised to quantify the
11 extent of fire-sensitive vegetation communities that have been affected. However, there is concern that these
12 interventions may have adverse effects on the values of the TWWHA if applied widely, so while research is
13 ongoing, these will only be applied in strategic areas (e.g., fire retardant is not being applied to some areas).
14
15 The TWWHA Management Plan (2016) emphasises Aboriginal fire management as an important value of
16 the area, along with their knowledge of plants, animals, marine resources, minerals (ochre and rock sources),
17 and their connection with the area as a living and dynamic landscape. Fire management planning aims to
18 protect important sites from fire and ensure that management does not impact Aboriginal cultural values
19 (DPIPWE, 2016). Increasingly, there is an acknowledgment that the cessation of traditional fire uses has led
20 to changes in vegetation and calls to incorporate Aboriginal burning knowledge into fire management of the
21 TWWHA.
22
23 2.6.5.9 Case Study: Bhojtal Lake, Bhopal, India
24
25 Scale: Local
26 Issue: Protection of water resources and biodiversity
27
28 The city of Bhopal, the capital of Madhya Pradesh state in central India, is dependent on Bhojtal, a large
29 man-made lake bordering the city, for its water supply (Everard et al., 2020). It is also an important
30 conservation site with wetlands protected under the Ramsar convention and diverse flora and fauna (WWF,
31 2006). Bhojtal also provides a wide range of other benefits to people, including tourism, recreation,
32 navigation, and subsistence and commercial fisheries, supporting the livelihoods of many families (Verma,
33 2001).
34
35 Climate change in Bhopal may pose ecological and socio-economic stresses due to changes in rainfall and
36 weather patterns (Anonymous, 2019), exacerbated by a series of problems such as wastewater discharge,
37 illegal digging of bore wells and unsustainable water extraction/exploitation (Everard et al., 2020).
38 Ecosystem service provision at Bhojtal was assessed using the Rapid Assessment of Wetland Ecosystem
39 Services (RAWES) approach, including water quality analysis from the lake. Information on the geology,
40 hydrology and catchment ecology of the lake was collected and a Baseline Biodiversity Assessment was
41 conducted.
42
43 The Lake Bhopal Conservation and Management Project (JICA, 2007) was developed with the following
44 actions:
45
46 1. Desilting and dredging; deepening and widening of spill channel; prevention of pollution (sewerage
47 scheme); management of shoreline and fringe area; improvement and management of water quality
48
49 2. Soil and water conservation measures using vegetative and engineering structures particularly at upper
50 ridges of watersheds; construction of small check dams or percolation tanks for recharge purposes in
51 areas marked for ‘drainage line recharge measures’.
52
53 3. Afforestation initiatives.
54
55 Implementation of these measures with the help of local communities improved the lake’s health. Nature-
56 based solutions are more resilient adaptation measures towards climate change. Restoration not only reduced
57 water stress but also provides multiple societal benefits in the urban area (Kabisch et al., 2016).
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1
2 2.6.5.10 Case Study: Addressing Vulnerability of Peat Swamp Forests in South East Asia (SEA)
3
4 Scale: Regional
5 Issue: Protecting peatland biodiversity, carbon and ecosystem services from climate change and land
6 degradation
7
8 SEA peatlands have undergone extensive logging, drainage and land-use conversion that have caused habitat
9 loss for endemic species, i.e., orangutan (Pongo spp) (Gregory et al., 2012; Struebig et al., 2015). Prolonged
10 droughts associated with El Niño (Section 4.4.3.2) compound the effects of drainage, leading to large
11 recurrent fires (Langner and Siegert, 2009; Gaveau et al., 2014; Putra et al., 2019). Under RCP 8.5, it is
12 projected that by the end of the century, the annual rainfall will significantly decrease (30%) over SEA, and
13 the number of consecutive dry days will significantly increase (60%) over Indonesia and Malaysia (Supari et
14 al., 2020). Peat degradation and losses to fire result in large GHG emissions (Miettinen et al., 2016) as well
15 as haze pollution that is a trans-boundary problem in the region (Heil et al., 2007).
16
17 Improving resilience to fire and climate change in SEA peatlands through restoration is extremely difficult
18 and presents many challenges. The Indonesian government has tasked the Badan Restorasi Gambut
19 (Peatland Restoration Agency) to restore peatlands (Darusman et al., 2021; Giesen, 2021). Other local
20 initiatives exist, such as fire management programmes and restoration projects (Puspitaloka et al., 2020).
21 Since 2016, the Government of Indonesia has rewetted ~380,000 hectares of degraded peatlands mainly
22 through canal blocking and flooding, but less than 2000 hectares were successfully restored to native plant
23 species common to peat swamp forests (Giesen, 2021).Replanting native trees has had relatively low success
24 (Lampela et al., 2017) because they have a low tolerance to prolonged inundation and a lack of fire
25 adaptation strategies (Page et al., 2009; Roucoux et al., 2013; Dohong et al., 2018; Cole et al., 2019; Luom,
26 2020; Giesen, 2021). Barriers to successful management are complex, and include the disparity in
27 timeframes between ecological restoration and political/socioeconomic needs (Harrison et al., 2020) and an
28 over-focus on fire-fighting rather than fire prevention (Mishra et al., 2021a). Early protection of peat forests
29 has been highlighted as a more effective management strategy than restoration, not only in insular SEA but
30 also in areas like Papua New Guinea, which may be targeted for expansion of estate crop plantations (Neuzil
31 et al., 1997; Dennis, 1999; Anshari et al., 2001; Anshari et al., 2004; Hooijer et al., 2006 Assessment of; Heil
32 et al., 2007; Page et al., 2009; Page et al., 2011; Posa et al., 2011; International, 2012; Miettinen et al., 2012;
33 Biagioni et al., 2015; Miettinen et al., 2016; Rieley and Page, 2016; Adila et al., 2017; Cole et al., 2019;
34 Vetrita and Cochrane, 2019; Harrison et al., 2020; Hoyt et al., 2020; Ruwaimana et al., 2020; Ward et al.,
35 2020; Cole et al., 2021).
36
37 2.6.6 Limits to Adaptation Actions by People
38
39 The evidence summarised above (Sections 2.6.2 - 2.6.4) shows that by restoring ecosystems it is possible to
40 increase their resilience to climate change, including the resilience of the populations of species they support
41 and of human communities. However, changes to healthy ecosystems and biodiversity are already happening
42 as described in this chapter (robust evidence, high agreement) and further changes are inevitable even under
43 low greenhouse gas emissions scenarios (robust evidence, high agreement). Planning to manage the
44 consequences of inevitable changes and prioritise investments in conservation actions where they have best
45 chance of succeeding (e.g. Section 2.6.4.6) will be an increasingly necessary component of adaptation
46 (robust evidence, high agreement) (Table 2.6).
47
48 It is possible to help species survive by active interventions such as translocation but as described above
49 (Section 2.6.4.1) it is not a straightforward process, is not suitable for all species and is resource intensive.
50 Modifying local microclimate or hydrological conditions can work for some species (Sections 2.6.2, 2.6.4.5),
51 but is likely to be less effective at higher levels of climate change. It will also be less successful for larger
52 species and more mobile ones. The microclimate of a tree is much more closely coupled with wider
53 atmospheric conditions than that of a small plant or animal in the boundary layer and mobile species such
54 birds and large mammals range over large areas rather than being confined to discrete locations where
55 conditions can be manipulated. There is potential for using evolutionary changes to enhance the adaptive
56 capacity for target species, such as is being done in the Great Barrier reef by translocating symbionts and
57 corals that have survived recent intense heat-induced bleaching events into areas that have had large die-off.
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1 However, known limitations to genetic adaptation preclude species-level adaptation to climates beyond their
2 ecological and evolutionary history (Sections 2.2.4.6; 2.6.1). All of these interventionist approaches are
3 constrained by requiring significant financial resources and expertise, they also require a high level of
4 understanding of individual species autecology, which can take years to acquire, even if resources were
5 available. Ex situ conservation (for example in seed banks may be the only option to conserve some species,
6 especially as levels of warming increase and this will not be feasible for all.
7
8 While the science of restoration has generated many successes, some habitats are very difficult to restore,
9 making certain decisions effectively irreversible. For example, Acacia nilotica was introduced into Indonesia
10 in the 1850s for gum arabic with planting expanded for fire breaks in the 1960s. This tree became invasive
11 and has already replaced >50% of savanna habitat in Baluran National Park, with complete replacement
12 expected in the near future. This shift from savanna to Acacia forest is causing large declines in native
13 species, including the charismatic wild banteng, Bos javanicus and wild dog (dhole, Cuon alpinus),
14 (Caesariantika et al., 2011; Padmanaba et al., 2017; Zahra et al., 2020). Multiple approaches to controlling
15 the spread of this Acacia have been ineffective, highlighting the difficulty of reversing the decision to plant
16 this tree (Zahra et al., 2020). Another example is the difficulties in restoring tropical peat forests of South
17 East Asia (Section 6.5.10).
18
19 EbA when implemented well can reduce risks to people but there are limits, for example, an extreme flood
20 event may exceed the capacity of natural catchments to hold water or slow its flow (Dadson et al., 2017),
21 urban shade trees and green space can make a few degrees difference to temperatures experienced by people
22 but that may not be enough in the hottest conditions.
23
24 In general adaptation measures can substantially reduce the adverse impacts of 1-2°C of global temperature
25 rise, but beyond this losses will increase (IPCC, 2018b), including species extinctions and changes, such as
26 major biome shifts which are cannot be reversed on human timescales. Some adaptation measures will also
27 become less effective at higher temperatures. Whilst adaptation is essential to reduce risks, it cannot be
28 regarded as a substitute for effective climate change mitigation (robust evidence, high agreement).
29
30 2.6.7 Climate Resilient Development
31
32 Climate Resilient Development (CRD) is the subject of Chapter 18. This section briefly assesses some of the
33 fundamental issues for CRD relating to ecosystems; an overview of the importance of specific ecosystem
34 services for CRD is presented in Box 18.7 in Chapter 18. A large body of evidence has demonstrated the
35 extent to which human life, well-being and economies are dependent on healthy ecosystems and the range of
36 threats they are under (high confidence) (IPBES, 2019; Dasgupta, 2021; Pörtner et al., 2021). An analysis of
37 64 studies found a strong positive synergy among eight critical regulating services of healthy ecosystems,
38 including climate regulation, water provisioning, pest and disease control and adjacent crop pollination (Lee
39 and Lautenbach, 2016). Health of ecosystems is, in turn, reliant upon maintenance of natural levels of
40 species' richness and functional diversity (high confidence) (Lavorel et al., 2020) (see Section 2.5.4). A
41 meta-analysis of 74 studies documented the mechanism for increased ecosystem stability was increased
42 asynchrony among species, that itself was a product of higher species diversity (Xu, 2021, consistently
43 positive effect). Responding to these threats requires the protection and restoration of natural and semi-
44 natural ecosystems, together with sustainable management of other areas.
45
46 The Convention on Biological Diversity set the Aichi 2020 target of 17% of each country to be protected for
47 biodiversity. Analyses suggest that 30% or even 50% of land and sea needs to be protected or restored to
48 confer adequate protection for species and ecosystem services (high confidence) (Pimm et al., 2018;
49 Dinerstein et al., 2019) (Woodley et al., 2019; Brooks et al., 2020; Hannah et al., 2020; Luther et al., 2020;
50 Zhao et al., 2020; Sala et al., 2021). Hannah (2020) estimated that limiting warming to 2°C and protecting
51 30% of high biodiversity regions (Africa, Asia and Latin America) reduced risk of species' extinctions in
52 half (medium confidence). Placement of protected areas is as important as total area (Pimm et al., 2018), and
53 quality of protection (strictness and enforcement) is as important as the official land designation (Shah et al.,
54 2021). Pimm et al. (Pimm et al., 2018) found that many small protected areas are successful because they are
55 in areas of very high biodiversity containing species of small ranges size, while many large regions identified
56 as wild often are of low biodiversity value, though they may have high mitigation value (e.g. high Arctic
57 tundra). Finally, a global meta-analysis of 89 restoration projects, biodiversity increased by 44% and
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1 ecosystem services by 25% after restoration, but values remained lower than in intact reference systems
2 (Benayas José et al., 2009).
3
4 There is also increasing evidence, reported in this chapter, that the loss and degradation of natural and semi-
5 natural habitats exacerbates the impacts of climate change and climatic extreme events on biodiversity and
6 ecosystem services (high confidence); example references include: (Ogden et al., 2013; Eigenbrod et al.,
7 2015; Struebig et al., 2015; Stevens et al., 2016; Oliver et al., 2017; McAlpine et al., 2018; Taffarello et al.,
8 2018; Lehikoinen et al., 2019; Birk et al., 2020; Chapman et al., 2020; Agol et al., 2021; Khaniya et al.,
9 2021; Lara et al., 2021; Lehikoinen et al., 2021). Considering these two sets of evidence together, it is clear
10 that climate change adaptation and ecosystem degradation both need to be addressed if either is to be tackled
11 successfully (robust evidence, high agreement) as a number of recent publications have concluded (Haddad
12 et al., 2015; Hannah et al., 2020; Arneth et al., 2021; Pörtner, 2021). Taking this combined body of evidence
13 together, this assessment is that the protection and restoration of natural and semi-natural ecosystems are key
14 adaptation measures (robust evidence, high agreement) (Section 2.5.4).
15
16 Large scale protection and restoration of ecosystems can also make a significant contribution to climate
17 change mitigation (Dinerstein et al., 2020; Roberts et al., 2020a; Soto-Navarro et al., 2020). Globally, there
18 is a 38% overlap between areas of high carbon storage and high intact biodiversity (mainly in the peatland
19 tropical forests of Asia, Western Amazon and the high Arctic), but only 12% of that is protected (high
20 confidence) (Soto-Navarro et al., 2020). Peatlands are particularly important carbon stores but are threatened
21 human disturbance, land use change (Leifeld et al., 2019) and fire (Turetsky et al., 2015). Restoration of
22 peatlands is not only an efficient nature climate solution in terms of GHG (Nugent et al., 2019), but it may
23 also increase ecosystem resilience (Glenk et al., 2021). Global restoration efforts are ongoing to target
24 degraded temperate peatlands in the American and Europe (Chimner et al., 2017), as a result of their
25 importance for climate change mitigation being recognised (Paustian et al., 2016; Bossio et al., 2020;
26 Humpenöder et al., 2020; Drever et al., 2021; Tanneberger et al., 2021). It has been estimated that the global
27 GHG saving potential of peatland restoration is similar to the most optimistic sequestration potential from all
28 agricultural soils (Leifeld and Menichetti, 2018). However the pressure on peatlands from human activity
29 remains high in many parts of the world (Humpenöder et al., 2020; Tanneberger et al., 2021). Currently, the
30 rapid destruction of tropical peatlands overshadows any current restoration efforts in temperate peatlands or
31 any potential carbon gain from natural high-latitude peatlands (Roucoux et al., 2017; Wijedasa et al., 2017;
32 Leifeld et al., 2019). (Sections 2.4.3.8, 2.4.4.4.2, 2.4.4.4.4, 2.5.2.8, 2.5.3.4)
33
34 Recent studies have highlighted the importance of ensuring that ecosystem protection is not implemented in
35 a way which disadvantages those who live in or depend on the most intact ecosystems (Mehrabi et al., 2018;
36 Schleicher et al., 2019) or risk food security. The actual area of land to be protected and the balance between
37 sustainable use and protection will need careful planning and targeting to where it can have most benefit
38 (Pimm et al., 2018); it will also be important to ensure that protection measures are effective in preventing
39 damage (Shah et al., 2021).
40
41 At a local level, EbA can often provide a wide range of additional benefits for sustainable development in
42 both rural and urban areas (Wilbanks, 2003; Nelson et al., 2007; Cohen-Shacham et al., 2016; Hobbie and
43 Grimm, 2020; Martín et al., 2020). A number of the case studies above, such as those in Durban and at
44 Bhojtal Lake illustrate this (2.6.5). A key element of Climate Resilient Development is ensuring that actions
45 taken to mitigate climate change do not compromise adaptation, biodiversity and human needs. This depends
46 on choosing appropriate actions for different locations (Box 2.2, Cross-Chapter Box NATURAL this
47 Chapter). A particularly notable case of this is woodland creation as described in Box 2.2: re-afforestation of
48 previously forested areas can provide multiple benefits (Lee et al., 2018; Lee et al., 2020) including for
49 climate change mitigation, adaptation and biodiversity. However planting trees where they would not
50 naturally grow can create multiple problems include the loss of native biodiversity and disruption of
51 hydrology (Box 2.2). It is also the case that protection of existing natural forest ecosystems is the highest
52 priority for reducing greenhouse gas emissions (Moomaw et al., 2019) and restoration may not always be
53 practical (see Section 2.6.4.10). (Sections 2.4.3.6, 2.4.3.7, 2.4.4.3, 2.4.4.4, 2.5.2.6, 2.5.2.7, 2.5.3.3, Box 2.2,
54 Cross-Chapter Box NATURAL this Chapter)
55
56 In some cases actions supported by international donors and presented as addressing climate change
57 adaptation and mitigation in the natural environment can have damaging consequences for people and nature
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1 as well as failing to deliver adaptation and mitigation. One example of this was presented by (Work et al.,
2 2019) who reviewed three climate change mitigation and adaptation projects in Cambodia: an irrigation
3 project, a protected-area forest management project, and a reforestation project. In each case they found
4 evidence of local communities rights being violated, maladaptation and destruction of biodiverse habitats.
5 They concluded that the potential for maladaptation and adverse social and environmental impacts had been
6 ignored by international donors as well as national authorities and that there was a need for much strict
7 accountability mechanisms. Moyo et al. (Moyo et al., 2021), using case studies from South Africa,
8 documented higher success of ecosystem restoration projects when they embraced broader SDGs,
9 particularly enhancement of people's livelihoods. Better assessment of the impacts of adaptation and
10 mitigation measures on people and ecosystems, before they are implemented, will be increasingly necessary
11 to avoid unintended and damaging consequences as their deployment is scaled up (Larsen, 2014; Enríquez-
12 de-Salamanca et al., 2017; Pour et al., 2017). This applies to ostensibly nature-based approaches as well as
13 more engineering-based ones.
14
15 Another aspect of the benefits to people from ecosystems that needs to be taken into account in Climate
16 Resilient Development is increasingly strong evidence of the benefits of natural environments for human
17 health and well-being beyond the provision of basic necessities, such as food and water (Bratman et al.,
18 2019; Marselle et al., 2021). Meta-analyses of 162 studies across 51,738 people documented that individuals
19 with high levels of contact with nature through their lives felt significantly happier, healthier and more
20 satisfied with their lives, and engage in more pro-nature behaviours, than those with little or no contact with
21 nature (high confidence) (Capaldi et al., 2014; Mackay and Schmitt, 2019; Pritchard et al., 2020; Whitburn et
22 al., 2020). Meta-analyses of manipulative human trials across 65 studies document a significant increase in
23 positive feelings and attitudes, and declines in negative feelings, after experimental treatments involving
24 nature (medium confidence) (Bowler et al., 2010b; McMahan and Estes, 2015; Soga et al., 2017). Within the
25 context of CRD improving the extent to which humans see themselves as part of the natural world – known
26 as human-nature connectedness (HNC) – by increasing access to natural areas, particularly within urban
27 areas, can provide additional health, cultural and recreation benefits of NbS as well as increasing public
28 engagement and support (robust evidence, high agreement) (Wilbanks, 2003; Nelson et al., 2007; Bowler et
29 al., 2010b; Capaldi et al., 2014; McMahan and Estes, 2015; Cohen-Shacham et al., 2016; Soga et al., 2017;
30 Mackay and Schmitt, 2019; Work et al., 2019; Hobbie and Grimm, 2020; Pritchard et al., 2020; Whitburn et
31 al., 2020).
32
33
34 [START CCB NATURAL HERE]
35
36 Cross-Chapter Box NATURAL: Nature-Based Solutions for Climate Change Mitigation and
37 Adaptation
38
39 Authors: Camille Parmesan (Chapter 2), Gusti Anshari (Chapter 2, CCP7), Polly Buotte (Chapter 4),
40 Donovan Campbell (Chapter 15), Edwin Castellanos (Chapter 12), Annette Cowie (WGIII Chapter 12),
41 Marta Rivera Ferre (Chapter 8), Patrick Gonzalez (Chapter 2, CCP3), Elena López Gunn (ch4), Rebecca
42 Harris (ch2, CCP3), Jeff Hicke (Chapter 14), Rachel Bezner Kerr (Chapter 5), Rodel Lasco (Chapter 5),
43 Robert Lempert (Chapter 1), Brendan Mackey (Chapter 11), Paulina Martinetto (Chapter 3), Robert
44 Matthews (WGIII, Chapter 3), Timon McPhearson (Chapter 6), Mike Morecroft (Chapter 2, CCP5), Aditi
45 Mukherji (Chapter 4), Gert-Jan Nabuurs (WGIII Chapter 7), Henry Neufeldt (Chapter 5), Roque Pedace
46 (WGIII Chapter 3), Julio Postigo (Chapter 12), Jeff Price (Chapter 2, CCP1), Juan Pulhin (Chapter 10), Joeri
47 Rogelj (WGI Chapter 5), Daniela Schmidt (Chapter 13), Dave Schoeman (Chapter 3), Pramod Kumar Singh
48 (Chapter 18), Pete Smith (WGIII Chapter 12), Nicola Stevens (Chapter 2, CCP3), Stavana E. Strutz (ch2),
49 Raman Sukumar (Chapter 1), Gautam Talukdar (Chapter 2, CCP1), Maria Cristina Tirado (Chapter 7),
50 Chapter ris Trisos (Chapter 9)
51
52 Nature-based Solutions provide adaptation and mitigation benefits for climate change as well as
53 contributing to other sustainable development goals (high confidence). Effective nature-based climate
54 mitigation stems from inclusive decision-making and adaptive management pathways that deliver climate-
55 resilient systems serving multiple sustainable development goals. Robust decision-making adjusts
56 management pathways as systems are impacted by on-going climate change. Poorly conceived and
57 designed nature-based mitigation efforts have the potential for multiple negative impacts, including
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1 competing for land and water with other sectors, reducing human well-being and failing to provide
2 mitigation that is sustainable in the long-term (high confidence).
3
4 The concept of Nature-based Solutions (NbS) is broad and debated but has become prominent in both the
5 scientific literature and policy since AR5, including earlier concepts, including Ecosystem based Adaptation
6 (EbA). The key point is that these are actions benefiting both people and biodiversity (IUCN 2020; WGII
7 Glossary). In the context of climate change, NbS provide adaptation and mitigation benefits for climate
8 change in ways that support wild species and habitats, often contributing to other sustainable development
9 goals (robust evidence, high agreement) (Keesstra et al., 2018; Lavorel et al., 2020; Malhi et al., 2020)
10 (Griscom et al., 2017; Hoegh-Guldberg et al., 2019; IPCC, 2019a; Lewis et al., 2019; Seddon et al.,
11 2020b)(AR6 WGIII Chapter 12, see Sections 2.2, 2.5.4, 2.6.3, 2.6.5, 2.6.7). Well-designed and implemented
12 NbS mitigation schemes can increase carbon uptake or reduce greenhouse gases emissions at the same time
13 as protecting or restoring biodiversity and incorporating elements of food provisioning (Mehrabi et al.,
14 2018). A variety of measures can be part of NbS, ranging from the protection of natural terrestrial,
15 freshwater and marine ecosystems, the restoration of degraded ones (this Cross-Chapter Box; Section 13.3),
16 to more sustainable management of naturally regenerating ecosystems used for food, fibre and energy
17 production (Figure Cross-Chapter Box NATURAL 1, Chapter 5, Cross-Working Group Box
18 BIOECONOMY in Chapter 5). Agroecological practices mitigate and adapt to climate change and can
19 promote native biodiversity (high confidence) (Sinclair et al., 2019; Snapp et al., 2021).
20
21 The role of restoration in NbS
22
23 Where natural ecosystems have been degraded or destroyed, re-establishing them and restoring natural
24 processes can be a key action for adaptation and mitigation, and the science of restoration is well-established
25 (de los Santos et al., 2019; Duarte et al., 2020) (Section 13.4.1). Such restoration activities need to adapt to
26 on-going climate change risks for the landscape and ocean scape and the species composition of biological
27 communities. Indeed, climate-change impacts may overwhelm attempts at restoration/conservation of
28 previous or existing ecosystems, particularly when the ecosystem is already near its tipping point, as are
29 tropical coral reefs (Bates et al., 2019; Bruno et al., 2019).
30
31 Lands (e.g.forests) and oceans (e.g. fisheries) managed for products using sustainable practices (whether
32 applied by private, state, or indigenous peoples) can also be carbon- and biodiversity-rich, and so effective
33 NbS (Paneque-Gálvez et al., 2018; Soto-Navarro et al., 2020). Indigenous people and private forest owners
34 manage, use or occupy at least one-quarter of global land area, over one-third of which overlaps with
35 protected areas, thus combining both protection and production (Jepsen et al., 2015; Garnett et al., 2018;
36 IPBES, 2019; Santopuoli et al., 2019).
37
38 Protection/restoration of natural systems, including reducing non-climate stressors, and sustainable
39 management of semi-natural areas emerge as necessary actions for adaptation to minimise extinctions of
40 species, reaching tipping points that cause regime shifts in natural systems, loss of whole ecosystems, and
41 their associated benefits for humans (Scheffer et al., 2001; Folke et al., 2005; Luther et al., 2020) (Chapters 2
42 and 3, AR6 WGIII Chapter 7). Such measures are critical for conservation of biodiversity and the provision
43 of ecosystem goods and services in the face of projected climate change (Duarte et al., 2020). Supporting
44 local livelihoods and providing benefits to indigenous, local communities and millions of private
45 landowners, together with their active engagement in decision-making, is critical to ensuring support for
46 NbS and its successfully delivery (high confidence) (Chapter 05; Figure Cross-Chapter Box NATURAL 1;
47 Ceddia et al., 2015; Blackman et al., 2017; Nabuurs et al., 2017; Smith et al., 2019a; Smith et al., 2019b;
48 Jones et al., 2020a; McElwee et al., 2020; Cao et al., 2021).
49
50 Forests
51
52 Intact natural forest ecosystems are major stores of carbon and support large numbers of species that cannot
53 survive in degraded habitats (very high confidence). Extensive areas of natural forest ecosystems remain in
54 tropical, boreal and (to a lesser extent) temperate biomes regions, but in many regions, they are managed
55 (sustainably and unsustainably) or have been degraded or cleared. Deforestation and degradation continue to
56 be a source of global greenhouse gas emissions (very high confidence) (Friedlingstein et al., 2019).
57 Protecting existing natural forests and sustainable management of semi natural forests providing goods and
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1 services is a highly effective NbS (Bauhus et al., 2009) (high confidence). Natural forests and sustainably
2 managed diverse forests play important roles for climate change mitigation and adaptation while providing
3 many other ecosystem goods and services (very high confidence) (Bradshaw and Warkentin, 2015; Favero et
4 al., 2020; Mackey et al., 2020). Contributions to climate change mitigation are estimated at medians of 5-7
5 Gt CO2/y (Roe et al., 2019). Forests influence the water cycle at local, regional and global scales (Creed and
6 van Noordwijk, 2018) reducing surface runoff, increasing infiltration to groundwater and improving water
7 quality (Bruijnzeel, 2004; Zhou et al., 2015a; Ellison et al., 2017; Alvarez-Garreton et al., 2019). Recent
8 evidence shows that downwind precipitation is also influenced by evapo-transpiration from forests (Keys et
9 al., 2016; Ellison et al., 2017). Protecting existing natural forest and sustainably managing production
10 forests, in a holistic manner, can optimise the provision of the many functions forests fulfil for owners,
11 conservation, mitigation and for society as a whole (Bauhus et al., 2009; Nabuurs et al., 2013).
12
13 Reforestation of formerly forested land can help to protect and recover biodiversity and can be one of the
14 most practical and cost effective ways of sequestering and storing carbon (high confidence) (Nabuurs et al.,
15 2017; Hoegh-Guldberg et al., 2018b; Paneque-Gálvez et al., 2018; Smith et al., 2018; Cook-Patton et al.,
16 2020; Cowie et al., 2021; Drever et al., 2021). This can be achieved through planting or by allowing natural
17 colonisation by tree and shrub species. The most effective method to employ depends upon local
18 circumstances (such as the presence of remnant forest cover) or socio-cultural and management objectives.
19 Reforestation with climate-resilient native or geographically near species restores biodiversity at the same
20 time as sequestering large amounts of carbon (Lewis et al., 2019; Rozendaal et al., 2019). It can also restore
21 hydrological processes, improving water supply and quality (Ellison et al., 2017) and reducing risks of soil
22 erosion and floods (high confidence) (Locatelli et al., 2015).
23
24 Climate change may mean that in any given location, different species will be able to survive and become
25 dominant, and restoring the former composition of forests may not be possible (Sections 2.4; 2.5). Severe
26 disturbances such as insect/pathogen outbreaks, wildfires, and droughts, which are an increasing risk, can
27 cause widespread tree mortality resulting in sequestered forest carbon being returned to the atmosphere
28 (Anderegg et al., 2020; Senf and Seidl, 2021) thus suggesting we need to adapt (Sections 2.4, 2.5, 13.3
29 14.4.1, Box 14.1). Adaptation measures, such as increasing the diversity of forest stands through ecological
30 restoration rather than monoculture plantations can help to reduce these risks (confidence). When plantations
31 are established without effective landscape planning and meaningful engagement including free prior and
32 informed consent, they can present risks to biodiversity and the rights, well-being and livelihoods of
33 Indigenous and local communities, as well as being less climate-resilient than natural forests (very high
34 confidence) (Section 5.6; Corbera et al., 2017; Mori et al., 2021).
35
36 Afforesting areas such as savannahs and many temperate peatlands, which would not naturally be forested,
37 damages biodiversity and increase vulnerability to climate change (high confidence) so is not a Nature-based
38 Solution and can exacerbate greenhouse gas emissions (Sections 2.4.3.5, 2.5.2.5, Box 2.2 this Chapter).
39 Remote sensing based assessments of suitability for tree planting can over-estimate potential, due to failure
40 to adequately distinguish between degraded forest and naturally open areas (Bastin et al., 2019; Veldman et
41 al., 2019; Bastin et al., 2020; Sullivan et al., 2020).
42
43 Peatlands
44
45 Peatlands are naturally high-carbon ecosystems, which have built up over millennia. Draining, cutting and
46 burning peat lead to oxidation and the release of CO2 (very high confidence). Rewetting by blocking
47 drainage, preventing cutting and burning can reverse this process on temperate peatlands (medium
48 confidence) although can take many years (Bonn et al., 2016). Trees are naturally found on many tropical
49 peatlands and restoration can involve removing non-native species such as oil palm and re-establishing
50 natural forest. However, peatland tropical forest is difficult to fully restore, and native pond fish, that are
51 vital as a local food, often do not return. Protection of intact peat forests, rather than attempting to restore
52 cleared forest, is by far the more effective pathway both in terms of cost, CO2 mitigation, and protection of
53 food sources (Kreft and Jetz, 2007). Naturally treeless temperate and boreal peatlands have in some cases
54 been drained to enable trees to be planted, which leads to CO2 emissions, and restoration requires removal of
55 trees as well as re-blocking drainage. (high confidence) (Sections 2.4.3.8; 2.5.4.8; 2.6.5.10).
56
57 Blue Carbon
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1
2 Blue Carbon ecosystems (mangroves, saltmarshes and seagrass meadows; see glossary) often have high rates
3 of carbon accumulation and sequestration (Section 3.5.5.5; Macreadie et al., 2019). However, quantification
4 of their overall mitigation value is difficult due to variable production of CH4 and N2O (Adams et al., 2012;
5 Rosentreter et al., 2018; MacLean et al., 2019b), uncertainties regarding the provenance of carbon
6 accumulated (Macreadie et al., 2019), and the release of CO2 by biogenic carbonate formation in seagrass
7 ecosystems (Saderne et al., 2019). Therefore, blue carbon strategies, referring to climate change mitigation
8 and adaptation actions based on conservation and restoration of blue carbon ecosystems, can be effective
9 NbS, with evidence of recovery in carbon stocks following restoration, although their global or regional
10 carbon sequestration potential and net mitigation potential may be limited (medium confidence) (Sections
11 3.6.3.1.6; 13.4.3, AR6 WGI 5.6.2.2.2; Duarte et al., 2020). They can also significantly attenuate wave
12 energy, raise the seafloor thus counteracting sea level rise effects, and buffer storm surges and flooding
13 erosion (high confidence) (Sections 13.2.2; 13.10.2). Additionally, they provide a suite of cultural (for
14 example, tourism, livelihood and well-being for native and local communities), provision (e.g. mangrove
15 woods, edible fish and shellfish) and regulation (e.g. nutrient cycling) services (high confidence) (Section
16 3.5.5.5). These services have motivated the implementation of management and conservation strategies of
17 these ecosystems (Sections 3.6.3.1.6; 13.4.2). Blue carbon strategies are relatively new, with many of them
18 experimental and small scale; therefore there is limited evidence of their long-term effectiveness. There is
19 also limited information on the potential emission of other GHGs from restored blue carbon ecosystems,
20 although reconnecting hydrological flow in mangroves and saltmarsh restoration are effective interventions
21 to reduce CH4 and CO2 (limited evidence, medium agreement) (Kroeger et al., 2017; Al-Haj and Fulweiler,
22 2020).
23
24 Urban NbS
25
26 Nature-based Solutions can be a key part of urban climate adaptation efforts. Direct human adaptation
27 benefit may stem from the cooling effects of urban forests and green spaces (parks and green roofs), from
28 coastal wetlands and mangroves reducing storm surge and flooding, and from sustainable drainage systems
29 designed to reduce surface flooding from extreme rainfall, as well as general benefits to human health and
30 well-being (high confidence) (Sections 2.2; 2.6; Chapter 6; Frantzeskaki et al., 2019; Keeler et al., 2019)
31 (Kowarik, 2011). Not all green schemes are considered "Nature Based Solutions" if they do not benefit
32 biodiversity, but carefully designed urban greening can be effective NbS. Careful planning also helps limit
33 negative equity consequences, benefiting wealthy neighbourhoods more than poor (Geneletti et al., 2016;
34 Pasimeni et al., 2019; Grafakos et al., 2020). Effective planning should also consider what is appropriate for
35 the climate and conditions of each city. For example, some trees emit volatiles (e.g. isoprene) that, in the
36 presence of certain atmospheric pollutants, can increase surface ozone that in turn can cause human
37 respiratory problems (Kreft and Jetz, 2007). Wetlands restoration close to human settlements needs to be
38 paired with mosquito control to prevent negative impacts on human health and well-being (Stewart-Sinclair
39 et al., 2020), but has been shown to provide better filtration and toxicity reduction with lower environmental
40 impact than other forms of waste-water treatment (Vymazal et al., 2021), including "green roofs" and "green
41 walls" (Chapter 06; Addo-Bankas et al., 2021).
42
43 Agroecological Farming
44
45 Agroecological farming (AF) is a holistic approach that incorporates ecologic and socio-economic
46 principles. It strives to enhance biodiversity, soil health and synergies between agroecosystem components,
47 reduce reliance on synthetic inputs (e.g., pesticides), builds on Indigenous knowledge and local knowledge,
48 and fosters social equity (e.g., supporting fair, local markets (HLPE, 2019; Wezel et al., 2020). AF practices
49 include intercropping, mobility of livestock grazing across landscapes, organic agriculture, integration of
50 livestock, fish and cropping, cover crops and agroforestry. (Sections 5.14; FAQ 12.5, 13.5.)
51
52 Agroforestry, cover crops and other practices that increase vegetation cover and enhance soil organic matter,
53 carefully management and varying by agroecosystem, mitigate climate change (high confidence) (Zomer et
54 al., 2016; Aryal et al., 2019; Nadège et al., 2019). Global meta-analyses demonstrate agroforestry storing 20
55 -33% more soil carbon than conventional agriculture (De Stefano and Jacobson, 2018; Shi et al., 2018) and
56 reducing the spread of fire (Sections 5.6, 13.5.2, 7.4.3, Box 7.7). Minimising synthetic inputs such as N-
57 based fertilisers reduces emissions (Gerber et al., 2016). Cover crops can reduce N2O emissions and increase
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1 soil organic carbon (Abdalla et al., 2019). Conservation farming (no-till with residue retention and crop
2 rotation) increases soil organic carbon particularly in arid regions (Sun et al., 2020). Silvo-pastoral systems
3 (pastures with trees), and other practices that increase vegetation cover and enhance soil organic matter
4 increase sequestered carbon in vegetation and soils (Zomer et al., 2016; Aryal et al., 2019; Nadège et al.,
5 2019; Ryan et al. 2019). Agroecologically improved cropland and grazing land management have significant
6 mitigation potential, estimated at 2.8- 4.1 GtCO2e per year (Smith et al., 2020). (Sectiond 5.10, 5.14, Box
7 5.10, Cross Working-Group Box BIOECONOMY in Chapter 5; WGIII 7.4.3; Box 7.7).
8
9 AF enhances adaptation to climate change, including resilience to extreme events. Building organic matter
10 improves soil water-holding capacity and buffers against drought; increased perenniality and high levels of
11 ground cover reduce soil erosion during storms; agroforestry shelters stock and crops in heat waves;
12 landscape complexity and agrobiodiversity increase resilience to disease and pests and stabilise livestock
13 production and restoration of oyster reefs provides thermal refugia and storm surge protection (Allred et al.,
14 2014; Henry et al., 2018; Kremen and Merenlender, 2018; Beillouin et al. 2019; ; Kuyah et al. 2019; Gilby
15 et al., 2020; Niether et al. 2020; Richard et al. 2020; Howie and Bishop, 2021; Snapp et al. 2021) (;) .
16 Livestock mobility enables adjusting to increased climatic variability while maintaining pastoral systems’
17 productivity (Turner and Schlecht, 2019; Scoones, 2020). Thus, adoption of agroecology principles and
18 practices will be highly beneficial to maintaining healthy, productive food systems under climate change
19 (high confidence) (Sections 5.4.4; 13.5.2; FAQ 12.4).
20
21 AF practices such as hedgerows and polycultures maintain habitat and connectivity for biodiversity and
22 support ecosystem functioning under climate stress compared to conventional agriculture (high confidence),
23 Section 5.4.4.4; Buechley et al., 2015; Kremen and Merenlender, 2018; Albrecht et al., 2020). Increasing
24 farm biodiversity benefits pollination, pest control, nutrient cycling, water regulation and soil fertility (Snapp
25 et al. 2021; Beillouin et al. 2019; Tamburini et al. 2020). Biodiverse agroforestry systems increase ecosystem
26 services and biodiversity benefits compared to simple agroforestry and conventional agriculture (high
27 confidence); up to 45% more biodiversity and 65% more ecosystem services compared to conventional
28 timber, crop or livestock production in the Brazilian Atlantic Forest (Santos et al., 2019), including for birds
29 (M. Greenler and Ebersole, 2015; Karp et al., 2019), local tree species (Braga et al., 2019) and fewer
30 invasive exotic plants species (Cordeiro et al., 2018). AF includes conservation of semi-natural woodlands,
31 which can conserve bird predators of insect pests (Gonthier et al., 2019). Organic production increases insect
32 species richness and abundance in and around the farm, including essential pollinators (Sections 5.10, 12.6;
33 Kennedy et al., 2013; Haggar et al., 2015; Lichtenberg et al., 2017).
34
35 AF significantly improves food security and nutrition by increasing access to healthy, diverse diets and
36 rising incomes for food producers, through increased biodiversity of crops, animals, and landscapes (high
37 confidence) (Garibaldi et al., 2016; D'Annolfo et al., 2017; Isbell et al., 2017; Dainese et al., 2019; Bezner
38 Kerr et al. 2021). Livestock mobility improves the site-specific matching of animals’ needs with food
39 availability (Damonte et al., 2019; Mijiddorj et al., 2020; Postigo, 2021), and can generate a form of
40 rewilding that restores lost ecosystem functioning (Gordon et al., 2021). Conservation of crop wild relatives
41 in situ supports genetic diversity in crops for the range of future climate scenarios (Redden et al., 2015).
42
43 System-level agroecological transitions require policy support for farmer experimentation and knowledge
44 exchange, community-based participatory methodologies and market and policy measures e.g. public
45 procurement, local and regional market support, regulation or payments for environmental services (HLPE
46 2019; Snapp et al. 2021; Mier y Teran et al. 2018). Scientific consensus about the food security and
47 environmental implications of agroecological transitions at a global scale is lacking. Yields in agroforestry
48 and organic can be lower than high-input agricultural systems, but conversely, AF can boost productivity and
49 profit, varying by timeframe, socio-economic, political and ecosystem context (medium confidence) (Section
50 5.14; Muller et al., 2017; LaCanne, 2018; Barbieri et al., 2019; Rosa-Schleich et al. 2019; Smith et al.,
51 2019b; Smith et al., 2020). ;. Such contrasting results and the limited investment in agroecological research
52 to date make paramount assessing the global and regional impacts of agroecological transitions on food
53 production, ecosystems and economy (Section 5.14; DeLonge et al., 2016; Muller et al., 2017; Barbieri et al.,
54 2019).
55
56 Conclusions
57
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1 Nature–based Solutions provide adaptation and mitigation benefits for climate change as well as contributing
2 to other sustainable development goals (high confidence). NbS avoid further emissions and promote CO2
3 removal using approaches that yield long-lasting mitigation benefits and avoid negative outcomes for other
4 sustainable development goals. Poorly conceived and designed mitigation efforts have the potential for
5 multiple negative impacts: (1) They can have cascading negative effects on long-term mitigation by
6 promoting short term sequestration over existing long-term accumulated carbon stocks, (2) They can be
7 detrimental to biodiversity, undermining conservation adaptation, and (3) They can erode other ecosystem
8 services important for human health and well-being (high confidence). Conversely, well-designed and
9 implemented mitigation efforts have the potential to provide co-benefits in terms of climate-change
10 adaptation, as well as multiple goods and services, including conservation of biodiversity, clean and
11 abundant water resources, flood mitigation, sustainable livelihoods, food and fibre security, and human
12 health and well-being (high confidence). A key aspect to such 'smart' climate mitigation is implementation of
13 inclusive and adaptive management pathways (Section 1.4.2). These entail acceptance of the inherent
14 uncertainty in projections of future climate change, especially at a regional or local level, and using decision
15 making processes that keep open as many options as possible, for as long as possible, with periodic re-
16 evaluation to aid in choosing pathways forward even as systems are being impacted by on-going climate
17 change (Figure Cross-Chapter Box NATURAL 1; Cross-Chapter Box DEEP in Chapter 17; Section 1.4.2).
18
19
20
21 Figure Cross-Chapter Box NATURAL.1: Decision-making framework to co-maximise adaptation and mitigation
22 benefits from natural systems. Decision-making pathways are designed to add robustness in the face of uncertainties in
23 future climate change and its impacts. Emphasis is on keeping open as many options as possible, for as long as possible,
24 with periodic re-evaluation to aid in choosing pathways forward even as systems are being impacted by on-going
25 climate change
26
27
28 Table Cross-Chapter Box NATURAL.1: Assessment of benefits and tradeoffs between mitigation and strategies for
29 both biodiversity and human adaptation to future climate change. Best practices highlight approaches that lead to
30 maximal positive synergies between mitigation and adaptation; worst practices are those most likely to lead to negative
31 tradeoffs for adaptation. Many best practices have additional societal benefits beyond adaptation, such as food
32 provisioning, recreation and improved water quality. Mitigation Potential (Mit. Pot.) and Restoration Potential (Rest.
33 Pot.) are considered.
Worst practices and
Best practices and Additional societal
System Mit. Pot. Rest. Pot. negative adaptation References
adaptation benefits benefits
tradeoffs
Forests
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Boreal Medium Medium Maintain or restore Very large scale clear cuts, Providing goods and Drever et al.
Forests species and structural aiming for one or few tree services, improved (2021)
diversity, reduce fire risk, species, although boreal is air quality,
spatially separate wood characterised by few tree improved
production, and species and a natural fire hydrology, jobs
sustainably intensify risk
management in some
regions
Temperate Very high High Maintain or restore natural Planting large scale non- Providing goods and Sections 2.4.3;
forests species and structural native monocultures which services, jobs, 2.5; Box 2.2 ;
diversity, leading to more would lead to loss of improved hydrology Nabuurs et al.
biodiverse and resilient biodiversity and poor and biodiversity (2017); Roe et
system climate change resilience al. (2019);
Favero et al.
(2020)
Tropical wet High Moderate Maintain or restore natural Planting non-native Indiginous foods, Section 2.4.3
forests species and structural monocultures, loss of medicines and other Edwards et al.
diversity, high biodiversity, poor climate forest products, (2014)
biodiversity, more change resilience, soil including
resilient to climate change erosion sustainable selective
logging
Tropical dry High Moderate Integrated landscape Planting non-native Foli et al.
forests management monocultures, Loss of (2018)
biodiversity, poor climate
change resilience, soil
erosion
Tropical Very high Low Integrated landscape Cutting native rainforest and Forest pond fish are Section 2.4.3;
peatland management planting palm oil for major food for local 2.5; Smith et
forests biodiesel results in very communities al. (2019b)
high carbon emissions from
exposed peat soils
Blue Carbon AR6 WGI
5.6.2.2.2
Mangroves Moderate High Conservation, restoration Potential NH4 emissions Improved fisheries Sections
of hydrological flows, and biodiversity, 3.4.2.5;
revegetation with native coastal protection 3.5.5.5;
plants, livelihoods against SLR and 3.6.3.1 ;
diversification, landscape storm surges, Macreadie et
planning for landward and recreation and al. (2019);
upstream migration cultural benefits Duarte et al.
(2020);
Sasmito et al.
(2020)
Saltmarshes Moderate High Conservation, reduce Potential NH4 emissions Improved fisheries Sections
nutrient loads, restoration and biodiversity, 3.4.2.5;
of hydrological flows and protection against 3.5.5.5;
sediment delivery, SLR and storm 3.6.3.1 ;
revegetation with native surges, recreational Macreadie et
plants, landscape planning and cultural benefits al. (2019);
for landward and Duarte et al.
upstream migration (2020)
Seagrasses Moderate High Conservation; restoration; Potential NH4 emissions Improved fisheries Section
improve water quality and and biodiversity; 3.4.2.5;
reduce local stressors protection from 3.5.5.5;
(reduction of industrial shoreline erosion; 3.6.3.1 ; de los
sewage, anchoring and recreational benefits Santos et al.
trawling regulation) (2019);
Macreadie et
al. (2019);
Duarte et al.
(2020)
Urban Ecosystems
Urban forests Moderate Moderate Integrated landscape monoculture of an exotic Recreation &
to High* management. Species tree lowers resilience and aesthetics; WGII Chapter
richness (including reduces biodiversity stormwater 06
exotics) can be high absorption benefits;
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heat mitigation, air
quality
improvements
Urban Moderate* Moderate Integrated landscape Recreation & WGII Chapter
wetlands management. aesthetics; 06
stormwater
absorption; heat
mitigation; coastal
flood protection
Urban Moderate* Moderate Integrated landscape fertilized commercial grass Recreation & WGII Chapter
grasslands management monocultures often require aesthetics; 06
irrigation and are less stormwater
resilient to droughts than absorption; heat
native, mixed grasses and mitigation
forbs
Open grasslands &
savanna
Boreal & High Moderate Blocking drainage Inappropriate hydrological Improved water Sections 2.4.3;
Temperate channels; Raise water restoration, e.g., flood quality in some 2.5 ; Bonn et
Peatlands level to natural condition; surface depth greater than conditions. al. (2016);
remove planted trees; natural depth leading to Nugent
revegetation of bare peat; methane emissions (2019);
No burns; Increases Taillardat et
biodiversity resilience; al. (2020)
Reduce flood risk
Tropical Moderate High Control of feral Afforestation,over- Improved grazing Sections 2.4.3;
savannas and herbivores; Reintroduce grazing/stocking; No burns; potential for 2.5; Box 2.1 ;
grasslands indigenous burns; inappropriate placement and livestock and dairy Stafford et al.
(including reintroduce native design of watering points. production, (2017); Moura
rangelands) Herbivores, controlled All leads to loss of sustainable wildlife et al. (2019);
grazing; strategic design biodiversity, and resilience; harvests, Increased Shackelford et
of water-holes; soil erosion; water water security, al. (2021);
Community-based natural insecurity income from eco- Stringer et al.
resource management, tourism, medicanal (2021);
grass reseeding, clearing plants, fuel wood. Wilsey (2021)
of invasive and Enhanced food
encroaching woody plants security.
Temperate Moderate Moderate Integrated landscape Monoculture of introduced Sustainable harvest Sections 2.4.3;
Grasslands to High to High management; sustainable species; over-fertilising with of wildlife, livestock 2.5, Box 2.1;
and grazing; Community- chemical or organic and dairy Farai, (2017);
rangelands based natural resource amendments; Failure to production, wild Baker et al.
management; Native manage human-wildlife fruits, medicinal (2018);
grassland species more clashes; Failure to distribute plants, construction Homewood et
resistant to drought than income equitably; material, fuelwood; al. (2020)
introduced species inadequate enabling policy income from Wilsey (2021)
to facilitate integrated ecotourism
landscape management
Agroecologial High High Biodiverse systems at Poorly chosen species, Food security, Sections 5.4,
farming and (context landscape scale; practices and amendments human health, 5.10, 5.12,
aquaculture specific) participatory adaptation to can lead to low yields; livelihoods, socio- 5.14 ;
context; Short value Simplified agroforestry cultural benefits e.g. Coulibaly et
chains; Farmer incentives; systems or industrial scale culturally- al. (2017);
Biodiversity synergies; organic agriculture lacks appropriate foods. HLPE (2019);
reduced climate risk holistic system-wide Quandt et al.
approach. Over-fertilising (2019);
with organic amendments Sinclair et al.
(2019); Smith
et al. (2019b);
Muchane et al.
(2020);
Reppin et al.
(2020)
1
2 [END CROSS-CHAPTER BOX NbS-NATURAL HERE]
3
4
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1 [INSERT FAQ2.5 HERE]
2
3 FAQ2.5: How can we reduce the risk of climate change to people by protecting and managing nature
4 better?
5
6 Damage to our natural environment can increase the risk climate change poses to people. Protecting and
7 restoring nature can be a way to adapt to climate change, with benefits for both humans and biodiversity.
8 Examples include reducing flood risk by restoring catchments and coastal habitats, the cooling effects of
9 natural vegetation and shade from trees and reducing the risk of extreme wildfires by better managing of
10 natural fires.
11
12 Protecting and restoring natural environments, such as forests and wetlands can reduce the risks climate
13 change poses to people, as well as supporting biodiversity, storing carbon and providing many other benefits
14 for human health and wellbeing. Climate change is bringing an increasing number of threats to people,
15 including flooding, droughts, wildfire, heat waves and rising sea levels. These threats can however be
16 reduced or aggravated, depending on how land, sea and freshwater are managed or protected. There is now
17 clear evidence that ‘Nature-based Solutions’ (NbS) can reduce the risks that climate change presents to
18 people. This is sometimes called ‘Ecosystem-based Adaptation (EbA) and includes::
19
20 • Natural flood management: As warm air holds more water, and in some places, because of changing
21 seasonal rainfall patterns, we are seeing more heavy downpours in many parts of the world. This can
22 create serious flooding problems, with loss of life, homes and livelihoods. The risk of flooding is
23 higher where natural vegetation has been removed, wetlands drained or channels straightened. In
24 these circumstances, water flows quicker and the risk of flood defenses being breached is increased.
25 Restoring the natural hydrology of upstream catchments, including by restoring vegetation, creating
26 wetlands and re-naturalising watercourse channels and reinstating connections with the flood plain
27 can reduce this risk. In a natural catchment with trees or other vegetation, water flows slowly
28 overland and much of it soaks into the soil. When the water reaches a watercourse, it moves slowly
29 down the channel, both because of the longer distance it travels when the channel bends and because
30 vegetation and fallen trees slow the flow. Wetlands, ponds and lakes can also hold water back and
31 slowly release it into river systems.
32
33 • Restoring natural coastal defences: Rising sea levels as a result of climate change, mean that coasts
34 are eroding at a fast rate and storm surges are more likely to cause damaging coastal flooding.
35 Natural coastal vegetation, such as saltmarsh and mangrove swamps can, in the right places, stabilise
36 the shoreline and act as a buffer, absorbing the force of waves. On a natural coast, the shoreline will
37 move inland and as sea level rises, the coastal vegetation will gradually move inland with it. This
38 contrasts with hard coastal defences such as sea walls and banks, which can be overwhelmed and
39 fail. In many places however, coastal habitats have been cleared and where there are hard sea
40 defences behind the coastal zone, the vegetation disappears as the coast erodes rather than moving
41 inland. This is often referred to as ‘coastal squeeze’ as the vegetation is squeezed between the sea
42 and the sea wall. Restoring coastal habitats and removing hard sea defences, can help reduce the
43 risks of catastrophic flooding.
44
45 • Providing local cooling: Climate change is bringing higher temperatures globally, which can result
46 in heatwaves affecting people’s health, comfort and agriculture. In cities, this can be a particular
47 problem for health as temperatures are typically higher than in the countryside. Trees give shade,
48 which people, in both rural and urban areas, have long used to provide cool places for themselves
49 and for crops such as coffee and livestock. Planting trees in the right place can be a valuable, low-
50 cost Natural-based Solution to reduce the effects of increasing heat, including in reducing water
51 temperatures in streams and rivers, which can help to maintain fisheries. Trees and other vegetation
52 also have a cooling effect as a result of water being lost from their leaves through evaporation and
53 transpiration (loss of water through pores in the leaves, known as stomata). Natural areas, parks,
54 gardens in urban areas can help reduce air temperatures by up to a few degrees.
55
56 • Restoring natural fire regimes: Some natural ecosystems are adapted to burning, such as savannas
57 and boreal forests. Where fire has been suppressed or non-native species of trees planted in more
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1 open habitats, there is a risk that potential fuel accumulates, which can result in larger and hotter
2 fires. Solutions can include restoring natural fire regimes and removing non-native species to
3 decrease people and ecosystems’ vulnerability to the exacerbated fire risk climate change is bringing
4 through higher temperatures and in some places changing rainfall patterns.
5
6 Nature-based Solutions, including protecting and restoring mangroves, forests and peatlands, also play an
7 important part in reducing greenhouse gas emissions and taking carbon dioxide out of the atmosphere. They
8 can also help people in a wide range of other ways, including through providing food, materials and
9 providing opportunities for recreation. There is increasing evidence that spending time in natural
10 surroundings is good for physical and mental health.
11
12 It is important that the right adaptation actions are carried out in the right place and that local communities
13 play an active part in making decisions about their local environment if Nature-based Solutions are to be
14 effective. When they are not part of the process, conflicts can emerge and benefits can be lost.
15
16 Whilst Nature-based Solutions help us to adapt to climate change and reduce the amount of greenhouse
17 gases in the atmosphere, it is important to note that there are limits to what they can do. To provide a safe
18 environment for both people and nature, it will be essential to radically reduce greenhouse gas emissions,
19 especially from fossil-fuel burning in the near future.
20
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1
2 Figure FAQ2.5.1: Different Nature-based Solutions strategies.
3
4 [END FAQ2.5 HERE]
5
6
7 2.6.8 Feasibility of Adaptation Options
8
9 IPCC (2018a) defined feasibility as “the degree to which climate goals and response options are considered
10 possible and/or desirable” (IPCC 2018) and set out an approach to assessing feasibility of pathways to limit
11 global temperature rise to 1.5 °C. (Singh et al., 2020) have developed this approach for adaptation,
12 recognising 6 different dimensions of feasibility: economic, technological, institutional, socio-cultural,
13 environmental / ecological and geophysical (Table 2.9). Feasibility is considered more fully in other chapters
14 of this report, including Cross-Chapter Box FEASIB in Chapter 18. Adaptation for biodiversity conservation
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1 and EbA encompasses a large range of approaches and techniques (Sections 2.6.2, 2.6.3) and will vary in
2 different contexts globally, as illustrated by the range of case studies (Section 2.6.5). It is important to take
3 account of specific regional and local circumstances as well as the type of adaptation action that envisaged
4 before making a feasibility assessment. It is also important to note that what is a feasible adaptation response
5 may change with the level of warming experienced – some techniques will be become less effective at higher
6 levels of warming. With global temperature rises of less than 2°C, in many cases it will be realistic to build
7 resilience and maintain species and ecosystems in situ, but at higher levels of warming, this will become
8 increasingly difficult and managing inevitable change, including the consequences of loss and damage will
9 be important (Prober et al., 2019). Similarly to be effective at higher levels of warming may require the
10 adaptation of EbA approaches themselves (Calliari et al., 2019; Martín et al., 2021; Ossola and Lin, 2021).
11 We have therefore not attempted a global scale assessment of the feasibility of adaptation options, but rather
12 present some key cross cutting considerations in assessing feasibility for adaptation for and through
13 ecosystems.
14
15 Many of the necessary techniques for climate change adaptation for biodiversity and EbA have been
16 demonstrated and shown to provide a wide range of additional benefits. This does however depend on
17 deploying the right techniques in the right place (Box 2.2) and the engagement of local communities (see
18 Section 2.6.6). There is also a challenge where there is high demand for land for other purposes, especially
19 for agriculture and urban developments. Table 2.8 summarise the main feasibility considerations, drawing on
20 previous sections. An assessment of constraints on EbA by Nalau et al. (2018) addressed similar issues.
21
22
23 Table 2.8: Considerations in assessing feasibility of ecosystem restoration for climate change adaptation, following
24 Singh et al. (2020)
Feasibility Feasibility indicators Factors relevant to ecosystem restoration
characteristics
Economic Micro-economic viability Costs are highly variable, depending on
Macro-economic viability techniques and whether land purchase is
required. Costs will depend on local rates for
Socio-economic vulnerability reduction labour and materials.
potential Economic benefits to local communities
Employment & productivity enhancement where employment is created and where loss
potential from extreme events are avoided. (Section
2.6.4; De Groot et al., 2013)
Technological Technical resource availability Techniques are available for restoration of
Risks mitigation potential (stranded Assets, most ecosystems (Sections 2.6.2; 2.6.3)
unforeseen Impacts) although it can be very difficult to achieve in
some circumstances and take a long time, e.g.
the restoration of peat swamp forests (Section
2.6.5.10).
Successful implementation may also require
skills which are in short supply and training
may be required.
Institutional Political acceptability This will vary according to local factors. It
Legal, Regulatory feasibility should however be noted that EbA and
adaptation for conservation has been
Institutional capacity & Administrative implemented in wide range of different
feasibility countries, including ones (see case studies in
Transparency & accountability potential Section 2.6.5). In many cases EbA can meet
multiple policy objectives but fall between
different decision makers responsibilities.
Socio-cultural Social co-benefits (Health, education) Multiple benefits to local communities are
Socio-Cultural acceptability possible but full engagement and/or
leadership of affected members of
Social & Regional Inclusiveness communities has been shown to be critical.
Benefits for gender equity Local Knowledge and Indigenous Knowledge
Intergenerational equity can provide important insights. (Section
2.6.6)
Ecological capacity
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Environmental/ Adaptive capacity/potential It is important to assess the benefits for
ecological ecosystems in relation to other potential
options. In particular for some EbA
approaches, it may be possible to achieve a
range of different outcomes for biodiversity.
Geophysical Physical Feasibility Appropriate measures need to be designed to
Land use change enhancement potential take account of local geophysical conditions,
for example catchment characteristics which
Hazard risk reduction potential define where some habitats can occur. This is
also critical for ensuring the effectiveness of
EbA in reducing natural hazards.
1
2
3 A key element of economic feasibility is the cost of adaptation options. Costs of adaptation vary greatly
4 depending on the actions taken, the location, the methods used, the need for ongoing maintenance and
5 whether land purchase is necessary. At its simplest adaptation may be a matter of taking account of actual or
6 potential climate change impacts in the course of conservation planning and have little or no additional cost.
7 For example, if a species of conservation concern colonises or starts to use a new area as a result of climate
8 change (for example, migrant waterfowl shifting the locations where they overwinter (Pavón-Jordán et al.,
9 2020b), protection or habitat management may be re-directed there. At the other extreme large scale
10 restoration can incur significant costs, for example between 1993 and 2015, the EU‐LIFE nature programme
11 invested 167.6M € in 80 projects, which aim to restore over 913 km2 of peatland habitats in Western
12 European countries (Andersen et al., 2017). This is equivalent to less than 2% of the remaining peatland
13 area, much of which has been affected to at least some extent by human pressures and restoring the total
14 areas will cost considerably more. De Groot et al. (2013) analysed 94 restoration projects globally and found
15 costs varied by several orders of magnitude but terrestrial and freshwater ecosystems mostly in the range of
16 100–10,000 USD per ha. They did however estimate that the majority of these projects provided net benefits
17 and should be considered as high yield investments. Some methods can however be much cheaper than
18 others even in the same type of ecosystems in the same Country: estimated cost of restoring forest cover in
19 Brazil varied between a mean of 49 USD using natural regeneration compared to a mean of 2041 USD per
20 hectare using planting (Brancalion et al., 2019). In assessing costs it is also important to take account of the
21 benefits delivered by different options, both in economic terms and other wider benefits.
22
23 The ‘technological’ dimension of feasibility in the context of ecosystems can be regarded as the range of
24 techniques available and the capacity to implement them. As described in Sections 2.6.2 and 2.6.3, above, a
25 wide range of techniques have been developed and are starting to be implemented. There is good evidence to
26 support adaptation for biodiversity and EbA in general terms and in many cases adaptation draws on
27 techniques for habitat creation and restoration which have been develop to mee other objectives. However,
28 feasibility needs to be assessed alongside likely effectiveness: a feasible but ineffective scheme is of no
29 value and the evaluation of success for specific interventions remains poorly developed (Morecroft et al.,
30 2019). It is often therefore important to proceed with the use of pilot studies and good monitoring and
31 evaluation of outcomes to build confidence before wider deployment of approaches. A linked technical area
32 is the availability of specialist skills and knowledge to implement adaptation which can vary considerably
33 according to the type of adaptation measure.
34
35 Institutional dimensions are dealt with more fully in other chapters, but in the specific context of the natural
36 environment, it is notable that EbA is relevant to a wide range of organisations and policy objectives, in
37 addition to environmental departments, NGOs and agencies, which conservation has traditionally been
38 delivered by. Upscaling implementation is likely to be dependent on this wider range of interests. There can
39 however be problems in that appropriate geographies for decision making on ecosystems (such as a
40 catchment) may not directly map onto governance arrangements
41
42 Socio-cultural factors are important in adaptation of the natural environment, in reviewing constraints on
43 EbA Nalau et al. (2018) found that risk perceptions and cultural preferences for particular types of
44 management approaches were frequently identified in studies.
45
46 In the IPCC feasibility assessment framework, one integral dimension is ‘Environmental / ecological’. In this
47 respect adaptation by and for ecosystems should perform well and this may be a reason to prefer EbA to
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1 other approaches when there is an alternative. It should however be noted that sometimes apparently
2 environmentally positive approaches such as forest creation can be done in ways which are damaging
3 (Section 2.6.7 and Box 2.2) and impacts need to be critically assessed for local circumstances.
4
5 Geophysical dimensions are important for ecosystems as they have typically shaped which ecosystems can
6 occur where and feasibility will depend on implementing adaptation options in places where they are
7 appropriate. Paleoecological studies can help inform potential options (Wingard et al., 2017)
8
9
10 [START BOX 2.2 HERE]
11
12 Box 2.2: Risks of Maladaptive Mitigation
13
14 To hold global temperature rise to well below 2°C and pursue efforts to limit it to 1.5°C as required by the
15 Paris Agreement requires major changes in land use and management. There are many opportunities for
16 Nature-based Solutions, which can provide climate change mitigation and adaptation in ways, which protect
17 and restore biodiversity and provide a wide range of benefits to people (Cross Chapter Box NATURAL this
18 Chapter). There are also new technologies and approaches to develop the bioeconomy in ways, which
19 provide many benefits (Cross-Working Group Box BIOECONOMY in Chapter 5). Nevertheless, renewable
20 energy is a large and essential element of the climate change mitigation and there are adverse impacts on
21 biodiversity associated with some renewable energy, including wind and solar technologies (Rehbein et al.,
22 2020) However, of the most serious emerging conflicts are between land-based approaches to mitigation
23 and the protection of biodiversity, particularly as a result of afforestation strategies and potentially large
24 areas devoted to bioenergy, including bioenergy with carbon capture and storage (BECCS). It is important to
25 recognise the impacts of climate change mitigation at the same time as assessing the direct impacts of
26 climate change and ensure that adaptation and mitigation are joined up.
27
28 BECCS is an integral part of all widely accepted pathways to holding global temperature rise to 1.5°C
29 (IPCC, 2018b). This requires large areas of land which can conflict with the need to produce food and
30 protect biodiversity (Smith et al., 2018). One study that examined the combined impacts of climate change
31 and land use change for bioenergy and found severe impacts on species were likely if bioenergy were a
32 major component of climate change mitigation strategies (Hof et al., 2018). A study on the potential impacts
33 of bioenergy production and climate change on European birds found that land conversion for biodiversity to
34 meet a 2°C target would have greater impacts on species range loss than a global temperature increase of
35 4°C, if bioenergy were the only mitigation option (Meller et al., 2015). To avoid the worst impacts of
36 BECCS, it will need to be carefully targeted, according to context and local conditions (and other mitigation
37 strategies prioritised so its use can be minimised IPCC, 2019, Special Report on Land; Ohashi, 2019,
38 Biodiversity can benefit).
39
40 Reforestation of formerly forested areas can bring multiple benefits, but planting trees in places where they
41 do not naturally grow can have serious environmental impacts, including potentially exacerbating the effects
42 of climate change. Savannas, are at amongst those that are at risk from afforestation programmes. Savannas
43 are grass dominated, high diversity ecosystems with endemic species adapted to high light environments,
44 herbivory and fire (Staver et al., 2011; Murphy et al., 2016). Interactions between climate change, elevated
45 CO2 and the disruption of natural disturbance regimes have led to widespread woody plant encroachment
46 (Stevens et al., 2016) causing a fundamental shift in ecosystem structure and function with loss of grass,
47 reduced fire frequency (Archibald et al., 2009) and streamflow (Honda and Durigan, 2016). Afforestation
48 exacerbates this degradation (Bremer and Farley, 2010; Veldman et al., 2015; Abreu et al., 2017). Global-
49 scale analyses aimed at identifying degraded forest areas suitable for afforestation (Veldman et al., 2019)
50 cannot reliably separate grassy ecosystems with sparse tree cover from degraded forests and local knowledge
51 is essential to ensure tree planting is targeted where it can most benefit and avoid harm. Figure Box 2.2.1
52 indicates where these issues are most likely to arise.
53
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2 Figure Box 2.2.1: Regions where savannas at potential risk from afforestation. Based on (Veldman et al., 2015)
3
4
5 A similar issue can occur in naturally treeless peatlands which can be afforested if they are drained, but this
6 leads to the loss of distinctive peatland species and communities as well as high greenhouse gas emissions
7 (Wilson et al., 2014). Mitigation benefits of growing timber are reduced or become negative in these
8 conditions by CO2 emissions from the oxidation of the drained peat - they can be a net sources rather than a
9 sinks (Simola et al., 2012; Crump, 2017; Goldstein et al., 2020).
10
11 [END BOX 2.2 HERE]
12
13
14 [START FAQ2.6 HERE]
15
16 FAQ2.6: Can tree planting tackle climate change?
17
18 Restoring and preventing further loss of native forests is essential for combating climate change. Planting
19 trees in historically unforested areas (grasslands, shrublands, savannas, some peatlands) can reduce
20 biodiversity and increase the risks of damage from climate change. It is therefore essential to target tree
21 planting to the appropriate locations and use appropriate species. Restoring and protecting forests reduces
22 human vulnerability to climate change, reduces air pollution, stores carbon and builds natural systems
23 resilience.
24
25 Like all living plants, trees remove carbon dioxide from the atmosphere through the process of
26 photosynthesis. In trees, this carbon uptake is relatively long-term, since much of it is stored in the trees’
27 woody stems and roots. Therefore, tree planting can be a valuable contribution to reducing climate change.
28 Besides capturing carbon, planting trees can reduce some negative impacts of climate change by providing
29 shade and cooling. It can also help prevent erosion and reduce flood risk by slowing water flow. Restoring
30 forest in degraded areas supports biodiversity and can provide benefits to people, ranging from timber to
31 food and recreation.
32
33 There are some areas where replacing lost trees is useful. These include forest that has been recently cut
34 down, and where reforestation is usually practical. However, it is very important to identify correctly areas
35 of forest that are degraded or have definitely been deforested. Reforesting places, especially where existing
36 native forest patches occur, brings benefits both in sucking up carbon from the atmosphere and helping us
37 adapt to climate change. Plantations of a non-native species, although offering economic benefits, do not
38 usually provide the same range of positive impacts and generally have lower biodiversity and carbon uptake
39 and storage.
40
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1 Reforestation options include the natural regeneration of the forest, assisted restoration, enrichment planting,
2 native tree plantations, commercial plantations and directed tree planting can occur in agroforestry systems
3 and urban areas. Reforestation with native species, usually contributes to a wide range of sustainability
4 goals, including biodiversity recovery, improved water filtration and groundwater recharge. It can reduce the
5 risks of soil erosion and flood risk. In cities, planting trees can support climate change adaptation by
6 reducing the heat of the area and can promote a wide range of social benefits such as providing shade and
7 benefiting outdoor recreation. Urban trees can also lower energy costs by reducing demand for conventional
8 sources of cooling like air-conditioning, especially during peak-demand periods. It is therefore important to
9 recognise that there are a wide range of different planting and forest management strategies. The choice will
10 depend on the objectives and the location.
11
12 Not everywhere is suitable for tree planting. It is particularly problematic in native non-forested ecosystems.
13 These natural ecosystems are not deforested and degraded but are instead naturally occurring non-forested
14 ecosystems. These areas vary from being open grasslands to densely wooded savannas and shrublands. Here
15 restoring the natural ecosystems instead of afforesting them will better contribute to increasing carbon
16 storage and increasing the areas resilience. It is important to remember, just because a tree can grow
17 somewhere, it does not mean that it should. These systems are very important in their own right, storing
18 carbon in soils, supporting a rich biodiversity and providing people with important ecosystem services such
19 as grazing. Planting trees in these areas destroys the ecosystem and threatens the biodiversity, which is
20 adapted to these environments. They can also impact on ecosystem services such as forage for livestock, on
21 which many people rely.
22
23 Many of these open areas also occur in low rainfall areas. Planting trees there uses a lot of water, can cause
24 reductions in stream flow and groundwater. Many of these locations also burn regularly and planting trees
25 threatens both the establishing trees but can also increase the intensity of the fires from that of a grass-fuelled
26 fire to that of a woody-fuelled fire. Swapping grassy ecosystems for forests may contribute to warming, as
27 forests absorb more incoming radiation (warmth) than grasslands. Aside from the negative impacts to
28 adaptation, it is also questionable just how much carbon can be sequestered in these landscapes asplanting
29 trees in grassy ecosystems can reduce carbon gains. Furthermore, a high belowground carbon store prevents
30 carbon loss to fire in these fire-prone environments.
31
32 Another example is with peatlands. Peats store an incredible amount of carbon within them and are therefore
33 important in maintaining and restoring to reduce atmospheric carbon. However, the restoration actions
34 depend on what type of peatland it is and where it is located. Many temperate and boreal peatlands are
35 naturally treeless. Here planting trees is often only possible following drainage, but draining and planting
36 (especially with non-native species) destroys native biodiversity and releases greenhouse gases. Yet many
37 peatlands, especially in the tropics, are naturally forested and restoring these peatlands requires re-wetting
38 and restoring natural tree cover (see Figure FAQ2.2.1) which will increase carbon storage.
39
40 There are actions we can do instead of planting trees in non-forested ecosystems, and these include:
41 • Address the causes of deforestation, forest degradation and widespread ecosystem loss;
42 • Reduce carbon emissions from fossil fuels;
43 • Focus on ecosystem restoration over tree planting. For example, in restoring tropical grassy
44 ecosystems, we can look at actions that cut down trees, enhance grass regrowth, and restore natural
45 fire regimes. We have then a much better chance of both enhancing carbon capture and reducing
46 some of the harmful effects of climate change.
47
48 In between the two extremes of where planting trees is highly suitable and areas where it is not, it is
49 important to remember that the context matters and decisions to (re)forest should look beyond simply the act
50 of planting trees. We can consider what the ecological, social and economic goals are of tree planting. It
51 is then important to verify the local context and then decide what restoration action will be most effective.
52 It is also very important to conserve forests before worrying about reforesting.
53
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2 Figure FAQ2.6.1: Some places are more appropriate for tree planting than others and caution needs to be applied when
3 planting in different biomes with some biomes being more suitable than others. This figure highlights some basic biome
4 specific guidelines when planting in natural and semi-natural vegetation.
5
6 [END FAQ2.6 HERE]
7
8
9 2.7 Reducing Scientific Uncertainties to Inform Policy and Management Decisions
10
11 Research since the IPCC Fifth Assessment Report (Settele et al., 2014) has increased understanding of
12 climate change impacts and vulnerability in ecosystems. Evidence gaps remain and geographic coverage of
13 research is uneven. This section assesses gaps in ecosystem science where research is necessary for
14 environmental policies and natural resource management, including under the UN Framework Convention
15 on Climate Change and the Convention on Biological Diversity.
16
17 2.7.1 Observed impacts
18
19 Detection and attribution efforts have yet to give robust assessments of the roles of climate change in
20 wildfires, tree mortality and human infectious diseases. Only one fire impact – the increase of the area
21 burned by wildfire in western North America in the period 1984-2017 (Section 2.4.4.2.1) and just a few
22 cases of tree mortality (Section 2.4.4.3) have been formally attributed to anthropogenic climate change.
23 Global changes in soil and freshwater ecosystem carbon over time remain uncertainties in global carbon
24 stocks and changes (Section 2.4.4.4); due to physical inabilities to conduct repeat monitoring and the lack of
25 remote sensing to scale up point measurements, no global methods can yet produce repeating spatial
26 estimates of soil carbon stock changes.
27
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1 Despite the growing understanding of the importance of ecosystem services this assessment found limited
2 research on observed impacts of climate change on ecosystem services for 14 of 18 ecosystem services
3 (Table 2.1)
4
5 2.7.2 Projected risks
6
7 A challenge for future projections that continues from previous IPCC reports is accurately characterising and
8 quantifying interactions among climate change and other factors causing ecological change, including
9 deforestation, agricultural expansion, urbanisation, and air and water pollution. Interactions can be
10 particularly complex for invasive species, pests and pathogens, and human infectious diseases. Modelling of
11 risks at the species level requires comprehensive databases of physiological, life-history, and functional traits
12 relevant to ecosystem resilience to climate change. Taxa that particularly lack this basis for model
13 projections include fungi and bacteria. For numerous plant and animal species, research on genotypic and
14 phenotypic diversity as a source of ecosystem resilience would inform projections of risk.
15
16 Soil plays a vital role in ecosystem function, is the habitat of a large number of species and is a large carbon
17 store which is currently a major source of greenhouse gas emissions, it is therefore a priority for climate
18 change research (Hashimoto et al., 2015). Major uncertainties remain in our understanding of soil functions.
19 Earth System Models (ESMs) predict soil respiration to increase with rising temperature (Friedlingstein et
20 al., 2014). However, there is evidence of acclimation post-increase (Carey et al., 2016) as the opposite
21 response of decrease in respiration with warming (Li et al., 2013; Reynolds et al., 2015). Long-term, large
22 scale field observations combined with a better conceptual understanding of factors governing soil process
23 responses to climate change is needed. A better understanding of plant water relations is also necessary,
24 including the response of plant transpiration to increased CO2, climate warming and changes in soil moisture
25 and groundwater elevation.
26
27 2.7.3 Adaptation and Climate Resilient Development
28
29 There are significant evidence gaps in developing adaptation, both for biodiversity conservation and EbA. In
30 particular, whilst many adaptation measures have been proposed and implementation is starting, there are
31 very few evaluations of success in the scientific literature (Morecroft et al., 2019; Prober et al., 2019). As
32 detailed in Section 2.6.2, there is a strong literature on conceptual approaches to climate change adaptation
33 for biodiversity, but very little empirical testing of which approaches actual work best. Going forward it is
34 important put in place good monitoring and evaluation of adaptation strategies. For EbA, there are good
35 examples of measuring changes in response to new adaptation measures, but these remain relatively rare
36 globally.
37
38 Human factors which promote or hinder adaptation are important as well as the technical issues. There are
39 few studies incorporating climate change and ecosystem services in integrated decision making, and even
40 fewer aimed to identify solutions robust to uncertainty (Runting et al., 2017).
41 Over the last decades, losses from natural disasters including those from events related to extreme weather
42 have strongly increased (Mechler and Bouwer, 2015). There is a need for better assessment of global
43 adaptation costs, funding and investment (Micale et al., 2018). Potential synergies between international
44 finance for disaster risk management and adaptation have not yet been fully realised. Research has almost
45 exclusively focused on normalising losses for changes in exposure, yet not for vulnerability, a major gap
46 given the dynamic nature of vulnerability (Mechler and Bouwer, 2015).
47
48
49
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